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• Preface to the Second Edition xiii Preface to the First Edition xv
• 1 DATA-MINING CONCEPTS 1 1.1 Introduction 1 1.2 Data-Mining Roots 4 1.3 Data-Mining Process 6 1.4 Large Data Sets 9 1.5 Data Warehouses for Data Mining 14 1.6 Business Aspects of Data Mining: Why a Data-Mining Project Fails 17 1.7 Organization of This Book 21 1.8 Review Questions and Problems 23 1.9 References for Further Study 24
• 2 PREPARING THE DATA 26 2.1 Representation of Raw Data 26 2.2 Characteristics of Raw Data 31 2.3 Transformation of Raw Data 33 2.4 Missing Data 36 2.5 Time-Dependent Data 37 2.6 Outlier Analysis 41 2.7 Review Questions and Problems 48 2.8 References for Further Study 51
• 3 DATA REDUCTION 53 3.1 Dimensions of Large Data Sets 54 3.2 Feature Reduction 56 3.3 Relief Algorithm 66 3.4 Entropy Measure for Ranking Features 68 3.5 PCA 70 3.6 Value Reduction 73 3.7 Feature Discretization: ChiMerge Technique 77 3.8 Case Reduction 80 3.9 Review Questions and Problems 83 3.10 References for Further Study 85
• 4 LEARNING FROM DATA 87 4.1 Learning Machine 89 4.2 SLT 93 4.3 Types of Learning Methods 99 4.4 Common Learning Tasks 101 4.5 SVMs 105 4.6 kNN: Nearest Neighbor Classifi er 118 4.7 Model Selection versus Generalization 122 4.8 Model Estimation 126 4.9 90% Accuracy: Now What? 132 4.10 Review Questions and Problems 136 4.11 References for Further Study 138
• 5 STATISTICAL METHODS 140 5.1 Statistical Inference 141 5.2 Assessing Differences in Data Sets 143 5.3 Bayesian Inference 146 5.4 Predictive Regression 149 5.5 ANOVA 155 5.6 Logistic Regression 157 5.7 Log-Linear Models 158 5.8 LDA 162 5.9 Review Questions and Problems 164 5.10 References for Further Study 167
• 6 DECISION TREES AND DECISION RULES 169 6.1 Decision Trees 171 6.2 C4.5 Algorithm: Generating a Decision Tree 173 6.3 Unknown Attribute Values 180 6.4 Pruning Decision Trees 184 6.5 C4.5 Algorithm: Generating Decision Rules 185 6.6 CART Algorithm & Gini Index 189 6.7 Limitations of Decision Trees and Decision Rules 192 6.8 Review Questions and Problems 194 6.9 References for Further Study 198
• 7 ARTIFICIAL NEURAL NETWORKS 199 7.1 Model of an Artifi cial Neuron 201 7.2 Architectures of ANNs 205 7.3 Learning Process 207 7.4 Learning Tasks Using ANNs 210 7.5 Multilayer Perceptrons (MLPs) 213 7.6 Competitive Networks and Competitive Learning 221 7.7 SOMs 225 7.8 Review Questions and Problems 231 7.9 References for Further Study 233
• 8 ENSEMBLE LEARNING 235 8.1 Ensemble-Learning Methodologies 236 8.2 Combination Schemes for Multiple Learners 240 8.3 Bagging and Boosting 241 8.4 AdaBoost 243 8.5 Review Questions and Problems 245 8.6 References for Further Study 247
• 9 CLUSTER ANALYSIS 249 9.1 Clustering Concepts 250 9.2 Similarity Measures 253 9.3 Agglomerative Hierarchical Clustering 259 9.4 Partitional Clustering 263 9.5 Incremental Clustering 266 9.6 DBSCAN Algorithm 270 9.7 BIRCH Algorithm 272 9.8 Clustering Validation 275 9.9 Review Questions and Problems 275 9.10 References for Further Study 279
• 10 ASSOCIATION RULES 280 10.1 Market-Basket Analysis 281 10.2 Algorithm Apriori 283 10.3 From Frequent Itemsets to Association Rules 285 10.4 Improving the Effi ciency of the Apriori Algorithm 286 10.5 FP Growth Method 288 10.6 Associative-Classifi cation Method 290 10.7 Multidimensional Association-Rules Mining 293 10.8 Review Questions and Problems 295 10.9 References for Further Study 298
• 11 WEB MINING AND TEXT MINING 300 11.1 Web Mining 300 11.2 Web Content, Structure, and Usage Mining 302 11.3 HITS and LOGSOM Algorithms 305 11.4 Mining Path-Traversal Patterns 310 11.5 PageRank Algorithm 313 11.6 Text Mining 316 11.7 Latent Semantic Analysis (LSA) 320 11.8 Review Questions and Problems 324 11.9 References for Further Study 326
• 12 ADVANCES IN DATA MINING 328 12.1 Graph Mining 329 12.2 Temporal Data Mining 343 12.3 Spatial Data Mining (SDM) 357 12.4 Distributed Data Mining (DDM) 360 12.5 Correlation Does Not Imply Causality 369 12.6 Privacy, Security, and Legal Aspects of Data Mining 376 12.7 Review Questions and Problems 381 12.8 References for Further Study 382
• 13 GENETIC ALGORITHMS 385 13.1 Fundamentals of GAs 386 13.2 Optimization Using GAs 388 13.3 A Simple Illustration of a GA 394 13.4 Schemata 399 13.5 TSP 402 13.6 Machine Learning Using GAs 404 13.7 GAs for Clustering 409 13.8 Review Questions and Problems 411 13.9 References for Further Study 413
• 14 FUZZY SETS AND FUZZY LOGIC 414 14.1 Fuzzy Sets 415 14.2 Fuzzy-Set Operations 420 14.3 Extension Principle and Fuzzy Relations 425 14.4 Fuzzy Logic and Fuzzy Inference Systems 429 14.5 Multifactorial Evaluation 433 14.6 Extracting Fuzzy Models from Data 436 14.7 Data Mining and Fuzzy Sets 441 14.8 Review Questions and Problems 443 14.9 References for Further Study 445
• 15 VISUALIZATION METHODS 447 15.1 Perception and Visualization 448 15.2 Scientifi c Visualization and Information Visualization 449 15.3 Parallel Coordinates 455 15.4 Radial Visualization 458 15.5 Visualization Using Self-Organizing Maps (SOMs) 460 15.6 Visualization Systems for Data Mining 462 15.7 Review Questions and Problems 467 15.8 References for Further Study 468 Appendix A 470 A.1 Data-Mining Journals 470 A.2 Data-Mining Conferences 473 A.3 Data-Mining Forums/Blogs 477 A.4 Data Sets 478 A.5 Comercially and Publicly Available Tools 480 A.6 Web Site Links 489 Appendix B: Data-Mining Applications 496 B.1 Data Mining for Financial Data Analysis 496 B.2 Data Mining for the Telecomunications Industry 499 B.3 Data Mining for the Retail Industry 501 B.4 Data Mining in Health Care and Biomedical Research 503 B.5 Data Mining in Science and Engineering 506 B.6 Pitfalls of Data Mining 509 Bibliography 510 Index 529.
• (source: Nielsen Book Data)

This book reviews state-of-the-art methodologies and techniques for analyzing enormous quantities of raw data in high-dimensional data spaces, to extract new information for decision making. Thegoal of this book is toprovide a single introductory source, organized in a systematic way, in which we could direct the readers in analysis of large data sets, through the explanation of basic concepts, models and methodologies developed in recent decades. If you are an instructor or professor and would like to obtain instructor's materials, please visit http://booksupport.wiley.com If you are an instructor or professor and would like to obtain a solutions manual, please send an email to: pressbooks@ieee.org.
(source: Nielsen Book Data)

• Chapter 1: The Changing World of UX Chapter 2: Data to the Rescue Chapter 3: Stats! It's Easier Than You Think Chapter 4: Unmoderated Remote Usability Tests Chapter 5: Surveys
• Chapter 6: Good Old Usability Tests
• Chapter 7: Persona Development
• Chapter 8: Field Studies
• Chapter 9: Live Website Data Chapter 10: Card Sorting Data
• Chapter 11: Case Studies - Tips from the Real World.
• (source: Nielsen Book Data)

Improving the User Experience through Practical Data Analytics shows you how to make UX design decisions based on data-not hunches. Authors Fritz and Berger help the UX professional recognize the enormous potential of user data that is collected as a natural by-product of routine UX research methods, including moderated usability tests, unmoderated usability tests, surveys, and contextual inquiries. Then, step-by-step, they explain how to utilize both descriptive and predictive statistical techniques to gain meaningful insight with that data. By mastering the use of these techniques, you'll delight your users, increase your bottom line and gain a powerful competitive advantage for your company-and yourself. Key features include: Practical advise on choosing the right data analysis technique for each project. A step-by-step methodology for applying each technique, including examples and scenarios drawn from the UX field. Detailed screen shots and instructions for performing the techniques using Excel (both for PC and Mac) and SPSS. Clear and concise guidance on interpreting the data output. Exercises to practice the techniques.
(source: Nielsen Book Data)

• Introduction What Is Computational Statistics? An Overview of the Book
• Probability Concepts Introduction Probability Conditional Probability and Independence Expectation Common Distributions
• Sampling Concepts Introduction Sampling Terminology and Concepts Sampling Distributions Parameter Estimation Empirical Distribution Function
• Generating Random Variables Introduction General Techniques for Generating Random Variables Generating Continuous Random Variables Generating Discrete Random Variables
• Exploratory Data Analysis Introduction Exploring Univariate Data Exploring Bivariate and Trivariate Data Exploring Multidimensional Data
• Finding Structure Introduction Projecting Data Principal Component Analysis Projection Pursuit EDA Independent Component Analysis Grand Tour Nonlinear Dimensionality Reduction
• Monte Carlo Methods for Inferential Statistics Introduction Classical Inferential Statistics Monte Carlo Methods for Inferential Statistics Bootstrap Methods
• Data Partitioning Introduction Cross-Validation Jackknife Better Bootstrap Confidence Intervals Jackknife-after-Bootstrap
• Probability Density Estimation Introduction Histograms Kernel Density Estimation Finite Mixtures Generating Random Variables
• Supervised Learning Introduction Bayes' Decision Theory Evaluating the Classifier Classification Trees Combining Classifiers Nearest Neighbor Classifier Support Vector Machines
• Unsupervised Learning Introduction Measures of Distance Hierarchical Clustering K-Means Clustering Model-Based Clustering Assessing Cluster Results
• Parametric Models Introduction Spline Regression Models Logistic Regression Generalized Linear Models Model Selection and Regularization Partial Least Squares Regression
• Nonparametric Models Introduction Some Smoothing Methods Kernel Methods Smoothing Splines Nonparametric Regression-Other Details Regression Trees Additive Models Multivariate Adaptive Regression Splines
• Markov Chain Monte Carlo Methods Introduction Background Metropolis-Hastings Algorithms The Gibbs Sampler Convergence Monitoring
• Appendix A: MATLAB (R) Basics Appendix B: Projection Pursuit Indexes Appendix C: Data Sets Appendix D: Notation
• References
• Index
• MATLAB (R) Code, Further Reading, and Exercises appear at the end of each chapter.
• (source: Nielsen Book Data)

• Introduction. Probability Concepts. Sampling Concepts. Generating Random Variables. Exploratory Data Analysis. Finding Structure. Monte Carlo Methods for Inferential Statistics. Data Partitioning. Probability Density Estimation. Supervised Learning. Unsupervised Learning. Parametric Models. Nonparametric Models. Markov Chain Monte Carlo Methods. Appendices. References. Index.
• (source: Nielsen Book Data)

A Strong Practical Focus on Applications and Algorithms Computational Statistics Handbook with MATLAB (R), Third Edition covers today's most commonly used techniques in computational statistics while maintaining the same philosophy and writing style of the bestselling previous editions. The text keeps theoretical concepts to a minimum, emphasizing the implementation of the methods. New to the Third Edition This third edition is updated with the latest version of MATLAB and the corresponding version of the Statistics and Machine Learning Toolbox. It also incorporates new sections on the nearest neighbor classifier, support vector machines, model checking and regularization, partial least squares regression, and multivariate adaptive regression splines. Web Resource The authors include algorithmic descriptions of the procedures as well as examples that illustrate the use of algorithms in data analysis. The MATLAB code, examples, and data sets are available online.
(source: Nielsen Book Data)
A Strong Practical Focus on Applications and AlgorithmsComputational Statistics Handbook with MATLAB, Third Edition covers today's most commonly used techniques in computational statistics while maintaining the same philosophy and writing style of the bestselling previous editions. The text keeps theoretical concepts to a minimum, emphasizing the i.
(source: Nielsen Book Data)

• Artificial intelligence for the real world / by Thomas H. Davenport and Rajeev Ronanki
• Stitch Fix's CEO on selling personal style to the mass market / by Katrina Lake
• Algorithms need managers too / by Michael Luca, Jon Kleinberg, Sendhil Mullainathan
• Marketing in the age of Alexa / by Niraj Dawar
• Why every company needs an augmented reality strategy / by Michael Porter
• Drones go to work / by Chris Anderson
• The truth about blockchain / by Marco Iansiti and Karim R. Lakhani
• The 3-D printing playbook / by Richard D'Aveni
• Collaborative intelligence: humans and AI are joining forces / by H. James Wilson and Paul Daugherty
• When your boss wears metal pants / by Walter Frick
• Managing our hub economy / by Marco Iansiti and Karim R. Lakhani.

Machine learning and data analytics are powering a wave of groundbreaking technologies. Is your company ready? If you read nothing else on how intelligent machines are revolutionizing business, read these 10 articles. We've combed through hundreds of Harvard Business Review articles and selected the most important ones to help you understand how these technologies work together, how to adopt them, and why your strategy can't ignore them. In this book you'll learn how: Data science, driven by artificial intelligence and machine learning, is yielding unprecedented business insights Blockchain has the potential to restructure the economy Drones and driverless vehicles are becoming essential tools 3-D printing is making new business models possible Augmented reality is transforming retail and manufacturing Smart speakers are redefining the rules of marketing Humans and machines are working together to reach new levels of productivity This collection of articles includes "Artificial Intelligence for the Real World, " by Thomas H. Davenport and Rajeev Ronanki; "Stitch Fix's CEO on Selling Personal Style to the Mass Market, " by Katrina Lake; "Algorithms Need Managers, Too, " by Michael Luca, Jon Kleinberg, and Sendhil Mullainathan; "Marketing in the Age of Alexa, " by Niraj Dawar; "Why Every Organization Needs an Augmented Reality Strategy, " by Michael E. Porter and James E. Heppelmann; "Drones Go to Work, " by Chris Anderson; "The Truth About Blockchain, " by Marco Iansiti and Karim R. Lakhani; "The 3-D Printing Playbook, " by Richard A. D'Aveni; "Collaborative Intelligence: Humans and AI Are Joining Forces, " by H. James Wilson and Paul R. Daugherty; "When Your Boss Wears Metal Pants, " by Walter Frick; and "Managing Our Hub Economy, " by Marco Iansiti and Karim R. Lakhani.
(source: Nielsen Book Data)

• Foreword xiii Preface xvii Acknowledgments xxiii About the Authors xxv
• Chapter 1 Overview 1 Warranties 3 Warranty Servicing 4 Warranty Costs 5 Warranty Fraud 6 Impact of Warranty Fraud 9 Warranty Fraud Management 10 Study of Warranty 10 Goals of the Book 12 Structure of the Book 12 Note 14
• Chapter 2 Products and Product Warranty 15 Products 16 Product Performance, Failure, and Reliability 19 Product Maintenance 24 Product Warranty 26 Maintenance Service Contracts 36 Insurances 37 Notes 38
• Chapter 3 Warranty Servicing 39 Parties in the Warranty Service Network 40 Warranty Service Process 46 Outsourcing of Warranty Service 54 Contracts 56 Notes 62
• Chapter 4 Warranty Costs 63 Different Perspectives 65 Factors Underlying Warranty Costs 68 Warranty Cost Metrics 72 Warranty Reserves and Accruals 77 Warranty Cost Control 78 Notes 79
• Chapter 5 Warranty Management 81 Evolution of Warranty Management 82 Service Life-Cycle Perspective 84 Product Life-Cycle Perspective 95 Organizational Structure 100 Warranty Management Systems 105 Warranty Management Maturity Models 122 Notes 124
• Chapter 6 Warranty Fraud 125 Fraud in General 126 Actors and Victims of Warranty Fraud 128 Classification of Warranty Fraud 129 Fraud Patterns 130 Consequences and Impacts of Warranty Fraud 135 Customer Fraud 139 Service Agent Fraud 147 Sales Channel Fraud 162 Warranty Administrator Fraud 166 Warranty Provider Fraud 169 Notes 175
• Chapter 7 Warranty Control Framework 177 Contracts 180 Transaction Controls 181 Analytics 183 Service Network Management 187
• Chapter 8 Customer Fraud Management 189 Customer Contract 190 Customer Entitlement 200 Material Returns Control 207 Analytics 208 Notes 213
• Chapter 9 Service Agent Fraud Management 215 Service Agent Contract 216 Entitlement and Repair Authorization Processes 237 Claim Validation Process 239 Analytics 248 Material Returns Control 278 Service Network Management 280 Notes 291
• Chapter 10 Fraud Management with Other Parties 293 Sales Channel Fraud Management 294 Warranty Administrator Fraud Management 299 Warranty Provider Fraud Management 305
• Chapter 11 Structures Influencing Warranty Fraud 307 Effective Service Process 308 Service Organization 315 Notes 318
• Chapter 12 Implementing a Warranty Control Framework 319 Assessing The Current Situation 320 Crafting an Improvement Plan 322 Defining Policies and Rules 322 Building the Capabilities 323 Deploying the Change 325 Business Case Considerations 327 Implementation Challenges 328 Achieving the Transformation 329
• Chapter 13 Epilogue 331 Opportunities to Improve Warranty Control 333 New Research into Warranty Fraud 335 Appendix A Detailed Claim Data 337 Appendix B Agency Theory 343 Appendix C Game Theory 347 Glossary 351 Acronyms 355 References 357 Index 363.
• (source: Nielsen Book Data)

Cut warranty costs by reducing fraud with transparent processes and balanced control Warranty Fraud Management provides a clear, practical framework for reducing fraudulent warranty claims and other excess costs in warranty and service operations. Packed with actionable guidelines and detailed information, this book lays out a system of efficient warranty management that can reduce costs without upsetting the customer relationship. You'll dig into the whole spectrum of warranty fraud, from simple sloppy procedures to systematic organized crime, and get to know the fraudulent parties, the victims, as well as the objectives and methods of the fraudulent activities in different scenarios. You'll learn how to implement controls to detect and reduce fraudulent claims and decrease the overall warranty costs. The impact of fraudulent claims is plainly spelled out alongside detailed descriptions of typical symptoms and process gaps present in diverse companies. A comprehensive, multi-modal framework for robust warranty management is presented as a template for revamping your own company's strategy. Fraudulent warranty claims occupy an estimated 3-15 percent of the average company's warranty costs, which generally average between 1-4 percent of sales. Many companies are unaware of the issue or struggle to take action against the claims for fear of upsetting business partners, or because they lack tangible evidence. This book details a robust warranty control framework that institutes transparency and control over the whole warranty chain supporting the process far beyond just fraud reduction. * Understand the different actors (customers, sales channels, service agents, warranty providers, etc.) and different forms of warranty fraud * Uncover issues in your company's warranty processes * Learn methods to detect and prevent fraudulent activities * Implement a robust system of warranty cost control Warranty fraud is a major cost-control issue for most companies, but the sensitive nature of the topic leaves most reluctant to share their experiences and divulge their strategies. Warranty Fraud Management brings warranty fraud out into the open, and provides a clear, actionable framework for cost-savings through fraud reduction.
(source: Nielsen Book Data)

• 1. Human Immune System and Infectious DiseaseFaruk Bin Poye
• n2. A Systematic Review of Predictive Models on COVID-19 with a Special Focus on CARD Modeling with SEI Formulation⁰́₄An Indian ScenarioSougata Mazumder, Debjit Majumder, and Prasun Ghosa
• l3. Data Analytics to Assess the Outbreak of the Coronavirus Epidemic: Opportunities and ChallengesMourade Azrour and Jamal Mabrouk
• i4. Leveraging Artificial Intelligence (AI) during the Coronavirus Pandemic: Applications and ChallengesPrabha Susy Mathew, Anitha S. Pillai, and Bindu Meno
• n5. Early Prediction of Coronavirus Epidemic Outbreak Using Stacked Long Short-Term Memory NetworksDebanjan Konar, Siddhartha Bhattacharyya, Sourav De, Aparajita Das, Jan Platos, Sergey V. Gorbachev, and Khan Muhamma
• d6. Use of Satellite Sensors to Diagnose Changes in Air Quality in Africa Before and During the COVID-19 PandemicLoubna Bouhachlaf, Jamal Mabrouki, Fatimazahra Mousli, Souad El Hajjaji, and Driss Dhib
• a7. Public Sentiments Analysis through Tweets on the COVID-19 Pandemic: A Comparative Study and Performance AssessmentBhagwati Prasad Pande and Koushal Kuma
• r8. Exploring Twitter Data to Understand the Impact of COVID-19 Pandemic in India Using NLP and Deep LearningRahul Deb Das, Ananda Sankar Pal, Madorina Paul, and Anjan Manda
• l9. Novel Coronavirus (COVID-19): Tracking, Health Care Precautions, Alerts, and Early WarningsAnupam Mondal and Naba Kumar Monda
• l10. Edge Computing-Based Smart Healthcare System for Home Monitoring of Quarantine Patients: Security Threat and Sustainability AspectsBiswajit Debnath, Adrija Das, Ankita Das, Rohit Roy Chowdhury, Saswati Gharami, and Abhijit Das

Intelligent Modeling, Prediction, and Diagnosis from Epidemiological Data: COVID-19 and Beyond is a handy treatise to elicit and elaborate possible intelligent mechanisms for modeling, prediction, diagnosis, and early detection of diseases arising from outbreaks of different epidemics with special reference to COVID-19. Starting with a formal introduction of the human immune systems, this book focuses on the epidemiological aspects with due cognizance to modeling, prevention, and diagnosis of epidemics. In addition, it also deals with evolving decisions on post-pandemic socio-economic structure. The book offers a comprehensive coverage of the most essential topics, including: A general overview of pandemics and their outbreak behavior A detailed overview of CI techniques Intelligent modeling, prediction, and diagnostic measures for pandemics Prognostic models Post-pandemic socio-economic structure The accompanying case studies are based on available real-world data sets. While other books may deal with this COVID-19 pandemic, none features topics covering the human immune system as well as influences on the environmental disorder due to the ongoing pandemic. The book is primarily intended to benefit medical professionals and healthcare workers as well as the virologists who are essentially the frontline fighters of this pandemic. In addition, it also serves as a vital resource for relevant researchers in this interdisciplinary field as well as for tutors and postgraduate and undergraduate students of information sciences

• PREFACE xi
• CHAPTER 1 AN INTRODUCTION TO DATA MINING 1 1.1 What is Data Mining? 1 1.2 Wanted: Data Miners 2 1.3 The Need for Human Direction of Data Mining 3 1.4 The Cross-Industry Standard Practice for Data Mining 4 1.4.1 Crisp-DM: The Six Phases 5 1.5 Fallacies of Data Mining 6 1.6 What Tasks Can Data Mining Accomplish? 8 1.6.1 Description 8 1.6.2 Estimation 8 1.6.3 Prediction 10 1.6.4 Classification 10 1.6.5 Clustering 12 1.6.6 Association 14 References 14 Exercises 15
• CHAPTER 2 DATA PREPROCESSING 16 2.1 Why do We Need to Preprocess the Data? 17 2.2 Data Cleaning 17 2.3 Handling Missing Data 19 2.4 Identifying Misclassifications 22 2.5 Graphical Methods for Identifying Outliers 22 2.6 Measures of Center and Spread 23 2.7 Data Transformation 26 2.8 Min-Max Normalization 26 2.9 Z-Score Standardization 27 2.10 Decimal Scaling 28 2.11 Transformations to Achieve Normality 28 2.12 Numerical Methods for Identifying Outliers 35 2.13 Flag Variables 36 2.14 Transforming Categorical Variables into Numerical Variables 37 2.15 Binning Numerical Variables 38 2.16 Reclassifying Categorical Variables 39 2.17 Adding an Index Field 39 2.18 Removing Variables that are Not Useful 39 2.19 Variables that Should Probably Not Be Removed 40 2.20 Removal of Duplicate Records 41 2.21 A Word About ID Fields 41 The R Zone 42 References 48 Exercises 48 Hands-On Analysis 50
• CHAPTER 3 EXPLORATORY DATA ANALYSIS 51 3.1 Hypothesis Testing Versus Exploratory Data Analysis 51 3.2 Getting to Know the Data Set 52 3.3 Exploring Categorical Variables 55 3.4 Exploring Numeric Variables 62 3.5 Exploring Multivariate Relationships 69 3.6 Selecting Interesting Subsets of the Data for Further Investigation 71 3.7 Using EDA to Uncover Anomalous Fields 71 3.8 Binning Based on Predictive Value 72 3.9 Deriving New Variables: Flag Variables 74 3.10 Deriving New Variables: Numerical Variables 77 3.11 Using EDA to Investigate Correlated Predictor Variables 77 3.12 Summary 80 The R Zone 82 Reference 88 Exercises 88 Hands-On Analysis 89
• CHAPTER 4 UNIVARIATE STATISTICAL ANALYSIS 91 4.1 Data Mining Tasks in Discovering Knowledge in Data 91 4.2 Statistical Approaches to Estimation and Prediction 92 4.3 Statistical Inference 93 4.4 How Confident are We in Our Estimates? 94 4.5 Confidence Interval Estimation of the Mean 95 4.6 How to Reduce the Margin of Error 97 4.7 Confidence Interval Estimation of the Proportion 98 4.8 Hypothesis Testing for the Mean 99 4.9 Assessing the Strength of Evidence Against the Null Hypothesis 101 4.10 Using Confidence Intervals to Perform Hypothesis Tests 102 4.11 Hypothesis Testing for the Proportion 104 The R Zone 105 Reference 106 Exercises 106
• CHAPTER 5 MULTIVARIATE STATISTICS 109 5.1 Two-Sample t-Test for Difference in Means 110 5.2 Two-Sample Z-Test for Difference in Proportions 111 5.3 Test for Homogeneity of Proportions 112 5.4 Chi-Square Test for Goodness of Fit of Multinomial Data 114 5.5 Analysis of Variance 115 5.6 Regression Analysis 118 5.7 Hypothesis Testing in Regression 122 5.8 Measuring the Quality of a Regression Model 123 5.9 Dangers of Extrapolation 123 5.10 Confidence Intervals for the Mean Value of y Given x 125 5.11 Prediction Intervals for a Randomly Chosen Value of y Given x 125 5.12 Multiple Regression 126 5.13 Verifying Model Assumptions 127 The R Zone 131 Reference 135 Exercises 135 Hands-On Analysis 136
• CHAPTER 6 PREPARING TO MODEL THE DATA 138 6.1 Supervised Versus Unsupervised Methods 138 6.2 Statistical Methodology and Data Mining Methodology 139 6.3 Cross-Validation 139 6.4 Overfitting 141 6.5 BIAS Variance Trade-Off 142 6.6 Balancing the Training Data Set 144 6.7 Establishing Baseline Performance 145 The R Zone 146 Reference 147 Exercises 147
• CHAPTER 7 k-NEAREST NEIGHBOR ALGORITHM 149 7.1 Classification Task 149 7.2 k-Nearest Neighbor Algorithm 150 7.3 Distance Function 153 7.4 Combination Function 156 7.4.1 Simple Unweighted Voting 156 7.4.2 Weighted Voting 156 7.5 Quantifying Attribute Relevance: Stretching the Axes 158 7.6 Database Considerations 158 7.7 k-Nearest Neighbor Algorithm for Estimation and Prediction 159 7.8 Choosing k 160 7.9 Application of k-Nearest Neighbor Algorithm Using IBM/SPSS Modeler 160 The R Zone 162 Exercises 163 Hands-On Analysis 164
• CHAPTER 8 DECISION TREES 165 8.1 What is a Decision Tree? 165 8.2 Requirements for Using Decision Trees 167 8.3 Classification and Regression Trees 168 8.4 C4.5 Algorithm 174 8.5 Decision Rules 179 8.6 Comparison of the C5.0 and Cart Algorithms Applied to Real Data 180 The R Zone 183 References 184 Exercises 185 Hands-On Analysis 185
• CHAPTER 9 NEURAL NETWORKS 187 9.1 Input and Output Encoding 188 9.2 Neural Networks for Estimation and Prediction 190 9.3 Simple Example of a Neural Network 191 9.4 Sigmoid Activation Function 193 9.5 Back-Propagation 194 9.5.1 Gradient Descent Method 194 9.5.2 Back-Propagation Rules 195 9.5.3 Example of Back-Propagation 196 9.6 Termination Criteria 198 9.7 Learning Rate 198 9.8 Momentum Term 199 9.9 Sensitivity Analysis 201 9.10 Application of Neural Network Modeling 202 The R Zone 204 References 207 Exercises 207 Hands-On Analysis 207
• CHAPTER 10 HIERARCHICAL AND k-MEANS CLUSTERING 209 10.1 The Clustering Task 209 10.2 Hierarchical Clustering Methods 212 10.3 Single-Linkage Clustering 213 10.4 Complete-Linkage Clustering 214 10.5 k-Means Clustering 215 10.6 Example of k-Means Clustering at Work 216 10.7 Behavior of MSB, MSE, and PSEUDO-F as the k-Means Algorithm Proceeds 219 10.8 Application of k-Means Clustering Using SAS Enterprise Miner 220 10.9 Using Cluster Membership to Predict Churn 223 The R Zone 224 References 226 Exercises 226 Hands-On Analysis 226
• CHAPTER 11 KOHONEN NETWORKS 228 11.1 Self-Organizing Maps 228 11.2 Kohonen Networks 230 11.2.1 Kohonen Networks Algorithm 231 11.3 Example of a Kohonen Network Study 231 11.4 Cluster Validity 235 11.5 Application of Clustering Using Kohonen Networks 235 11.6 Interpreting the Clusters 237 11.6.1 Cluster Profiles 240 11.7 Using Cluster Membership as Input to Downstream Data Mining Models 242 The R Zone 243 References 245 Exercises 245 Hands-On Analysis 245
• CHAPTER 12 ASSOCIATION RULES 247 12.1 Affinity Analysis and Market Basket Analysis 247 12.1.1 Data Representation for Market Basket Analysis 248 12.2 Support, Confidence, Frequent Itemsets, and the a Priori Property 249 12.3 How Does the a Priori Algorithm Work? 251 12.3.1 Generating Frequent Itemsets 251 12.3.2 Generating Association Rules 253 12.4 Extension from Flag Data to General Categorical Data 255 12.5 Information-Theoretic Approach: Generalized Rule Induction Method 256 12.5.1 J-Measure 257 12.6 Association Rules are Easy to do Badly 258 12.7 How Can We Measure the Usefulness of Association Rules? 259 12.8 Do Association Rules Represent Supervised or Unsupervised Learning? 260 12.9 Local Patterns Versus Global Models 261 The R Zone 262 References 263 Exercises 263 Hands-On Analysis 264
• CHAPTER 13 IMPUTATION OF MISSING DATA 266 13.1 Need for Imputation of Missing Data 266 13.2 Imputation of Missing Data: Continuous Variables 267 13.3 Standard Error of the Imputation 270 13.4 Imputation of Missing Data: Categorical Variables 271 13.5 Handling Patterns in Missingness 272 The R Zone 273 Reference 276 Exercises 276 Hands-On Analysis 276
• CHAPTER 14 MODEL EVALUATION TECHNIQUES 277 14.1 Model Evaluation Techniques for the Description Task 278 14.2 Model Evaluation Techniques for the Estimation and Prediction Tasks 278 14.3 Model Evaluation Techniques for the Classification Task 280 14.4 Error Rate, False Positives, and False Negatives 280 14.5 Sensitivity and Specificity 283 14.6 Misclassification Cost Adjustment to Reflect Real-World Concerns 284 14.7 Decision Cost/Benefit Analysis 285 14.8 Lift Charts and Gains Charts 286 14.9 Interweaving Model Evaluation with Model Building 289 14.10 Confluence of Results: Applying a Suite of Models 290 The R Zone 291 Reference 291 Exercises 291 Hands-On Analysis 291 APPENDIX: DATA SUMMARIZATION AND VISUALIZATION 294 INDEX 309.
• (source: Nielsen Book Data)

The field of data mining lies at the confluence of predictive analytics, statistical analysis, and business intelligence. Due to the ever-increasing complexity and size of data sets and the wide range of applications in computer science, business, and health care, the process of discovering knowledge in data is more relevant than ever before. This book provides the tools needed to thrive in today s big data world. The author demonstrates how to leverage a company s existing databases to increase profits and market share, and carefully explains the most current data science methods and techniques. The reader will learn data mining by doing data mining . By adding chapters on data modelling preparation, imputation of missing data, and multivariate statistical analysis, Discovering Knowledge in Data, Second Edition remains the eminent reference on data mining . * The second edition of a highly praised, successful reference on data mining, with thorough coverage of big data applications, predictive analytics, and statistical analysis. * Includes new chapters on Multivariate Statistics, Preparing to Model the Data, and Imputation of Missing Data, and an Appendix on Data Summarization and Visualization * Offers extensive coverage of the R statistical programming language * Contains 280 end-of-chapter exercises * Includes a companion website for university instructors who adopt the book.
(source: Nielsen Book Data)

• Preface ix Contributors xi
• 1 Introduction 1 Haibo He 1.1 Problem Formulation, 1 1.2 State-of-the-Art Research, 3 1.3 Looking Ahead: Challenges and Opportunities, 6 1.4 Acknowledgments, 7 References, 8
• 2 Foundations of Imbalanced Learning 13 Gary M. Weiss 2.1 Introduction, 14 2.2 Background, 14 2.3 Foundational Issues, 19 2.4 Methods for Addressing Imbalanced Data, 26 2.5 Mapping Foundational Issues to Solutions, 35 2.6 Misconceptions About Sampling Methods, 36 2.7 Recommendations and Guidelines, 38 References, 38
• 3 Imbalanced Datasets: From Sampling to Classifiers 43 T. Ryan Hoens and Nitesh V. Chawla 3.1 Introduction, 43 3.2 Sampling Methods, 44 3.3 Skew-Insensitive Classifiers for Class Imbalance, 49 3.4 Evaluation Metrics, 52 3.5 Discussion, 56 References, 57
• 4 Ensemble Methods for Class Imbalance Learning 61 Xu-Ying Liu and Zhi-Hua Zhou 4.1 Introduction, 61 4.2 Ensemble Methods, 62 4.3 Ensemble Methods for Class Imbalance Learning, 66 4.4 Empirical Study, 73 4.5 Concluding Remarks, 79 References, 80
• 5 Class Imbalance Learning Methods for Support Vector Machines 83 Rukshan Batuwita and Vasile Palade 5.1 Introduction, 83 5.2 Introduction to Support Vector Machines, 84 5.3 SVMs and Class Imbalance, 86 5.4 External Imbalance Learning Methods for SVMs: Data Preprocessing Methods, 87 5.5 Internal Imbalance Learning Methods for SVMs: Algorithmic Methods, 88 5.6 Summary, 96 References, 96
• 6 Class Imbalance and Active Learning 101 Josh Attenberg and S&cedil--eyda Ertekin 6.1 Introduction, 102 6.2 Active Learning for Imbalanced Problems, 103 6.3 Active Learning for Imbalanced Data Classification, 110 6.4 Adaptive Resampling with Active Learning, 122 6.5 Difficulties with Extreme Class Imbalance, 129 6.6 Dealing with Disjunctive Classes, 130 6.7 Starting Cold, 132 6.8 Alternatives to Active Learning for Imbalanced Problems, 133 6.9 Conclusion, 144 References, 145
• 7 Nonstationary Stream Data Learning with Imbalanced Class Distribution 151 Sheng Chen and Haibo He 7.1 Introduction, 152 7.2 Preliminaries, 154 7.3 Algorithms, 157 7.4 Simulation, 167 7.5 Conclusion, 182 7.6 Acknowledgments, 183 References, 184
• 8 Assessment Metrics for Imbalanced Learning 187 Nathalie Japkowicz 8.1 Introduction, 187 8.2 A Review of Evaluation Metric Families and their Applicability to the Class Imbalance Problem, 189 8.3 Threshold Metrics: Multiple- Versus Single-Class Focus, 190 8.4 Ranking Methods and Metrics: Taking Uncertainty into Consideration, 196 8.5 Conclusion, 204 8.6 Acknowledgments, 205 References, 205 Index 207.
• (source: Nielsen Book Data)

Solving imbalanced learning problems is critical in numerous data-intensive networked systems, including surveillance, security, Internet, finance, biomedical, and defense, to name a few. The first comprehensive look at this new branch of machine learning, this volume offers a critical review of the problem of imbalanced learning, covering the state-of-the-art in techniques, principles, and real-world applications. Scientists and engineers will learn how to tackle the problem of learning from imbalanced datasets, and gain insight into current developments in the field as well as future research direction.
(source: Nielsen Book Data)

• chapter 1 Data, Exploratory Analysis, and R / Ronald K. Pearson
• chapter 2 Graphics in R / Ronald K. Pearson
• chapter 3 Exploratory Data Analysis: A First Look / Ronald K. Pearson
• chapter 4 Working with External Data / Ronald K. Pearson
• chapter 5 Linear Regression Models / Ronald K. Pearson
• chapter 6 Crafting Data Stories / Ronald K. Pearson
• chapter 7 Programming in R / Ronald K. Pearson
• chapter 8 Working with Text Data / Ronald K. Pearson
• chapter 9 Exploratory Data Analysis: A Second Look / Ronald K. Pearson
• chapter 10 More General Predictive Models / Ronald K. Pearson
• chapter 11 Keeping It All Together / Ronald K. Pearson.

Exploratory Data Analysis Using R provides a classroom-tested introduction to exploratory data analysis (EDA) and introduces the range of "interesting" - good, bad, and ugly - features that can be found in data, and why it is important to find them. It also introduces the mechanics of using R to explore and explain data. The book begins with a detailed overview of data, exploratory analysis, and R, as well as graphics in R. It then explores working with external data, linear regression models, and crafting data stories. The second part of the book focuses on developing R programs, including good programming practices and examples, working with text data, and general predictive models. The book ends with a chapter on "keeping it all together" that includes managing the R installation, managing files, documenting, and an introduction to reproducible computing. The book is designed for both advanced undergraduate, entry-level graduate students, and working professionals with little to no prior exposure to data analysis, modeling, statistics, or programming. it keeps the treatment relatively non-mathematical, even though data analysis is an inherently mathematical subject. Exercises are included at the end of most chapters, and an instructor's solution manual is available. About the Author: Ronald K. Pearson holds the position of Senior Data Scientist with GeoVera, a property insurance company in Fairfield, California, and he has previously held similar positions in a variety of application areas, including software development, drug safety data analysis, and the analysis of industrial process data. He holds a PhD in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology and has published conference and journal papers on topics ranging from nonlinear dynamic model structure selection to the problems of disguised missing data in predictive modeling. Dr. Pearson has authored or co-authored books including Exploring Data in Engineering, the Sciences, and Medicine (Oxford University Press, 2011) and Nonlinear Digital Filtering with Python. He is also the developer of the DataCamp course on base R graphics and is an author of the datarobot and GoodmanKruskal R packages available from CRAN (the Comprehensive R Archive Network).
(source: Nielsen Book Data)

• 1. Introduction to IoT Security, IoT Architecture and Protocols.
• 2. Threats to IoT Devices and Security Challenges in IoT Integrations.
• 3. Secure Data Transmission in IoT Integrated System.
• 4. Cloud and IoT Integration.
• 5. Fog Computing and IoT Integrations.
• 6. Blockchain and IoT Integration.
• 7. AI and IoT integration.
• 8. Deep Learning for Cloud-Based Internet of Things.
• 9. Identity Access Management Models in IoT.
• 10. Programming Language and Hardware Support for Implementing IoT Integration.
• 11. Domain Specific Applications of IoT and Security Challenges.
• (source: Nielsen Book Data)

IoT is empowered by various technologies used to detect, gather, store, act, process, transmit, oversee, and examine information. The combination of emergent technologies for information processing and distributed security, such as Cloud computing, Artificial intelligence, and Blockchain, brings new challenges in addressing distributed security methods that form the foundation of improved and eventually entirely new products and services. As systems interact with each other, it is essential to have an agreed interoperability standard, which is safe and valid. This book aims at providing an introduction by illustrating state-of-the-art security challenges and threats in IoT and the latest developments in IoT with Cloud, AI, and Blockchain security challenges. Various application case studies from domains such as science, engineering, and healthcare are introduced, along with their architecture and how they leverage various technologies Cloud, AI, and Blockchain. This book provides a comprehensive guide to researchers and students to design IoT integrated AI, Cloud, and Blockchain projects and to have an overview of the next generation challenges that may arise in the coming years.
(source: Nielsen Book Data)

• Preface xv
• 1 Introduction 1
• 1.1 Case study: World Heritage Sites in Danger 1
• 1.2 Some remarks on web data quality 7
• 1.3 Technologies for disseminating, extracting, and storing web data 9
• 1.3.1 Technologies for disseminating content on the Web 9
• 1.3.2 Technologies for information extraction from web documents 11
• 1.3.3 Technologies for data storage 12
• 1.4 Structure of the book 13
• Part One A Primer onWeb and Data Technologies 15
• 2 HTML 17
• 2.1 Browser presentation and source code 18
• 2.2 Syntax rules 19
• 2.2.1 Tags, elements, and attributes 20
• 2.2.2 Tree structure 21
• 2.2.3 Comments 22
• 2.2.4 Reserved and special characters 22
• 2.2.5 Document type definition 23
• 2.2.6 Spaces and line breaks 23
• 2.3 Tags and attributes 24
• 2.3.1 The anchor tag 24
• 2.3.2 The metadata tag
• 25 2.3.3 The external reference tag 26
• 2.3.4 Emphasizing tags , , 26
• 2.3.5 The paragraphs tag
• 27
• 2.3.6 Heading tags , , , 27 2.3.7 Listing content with
• , , and
• 27 2.3.8 The organizational tags
• and 27 2.3.9 The
• tag and its companions 29 2.3.10 The foreign script tag.
• (source: Nielsen Book Data)

A hands on guide to web scraping and text mining for both beginners and experienced users of R Introduces fundamental concepts of the main architecture of the web and databases and covers HTTP, HTML, XML, JSON, SQL. Provides basic techniques to query web documents and data sets (XPath and regular expressions). An extensive set of exercises are presented to guide the reader through each technique. Explores both supervised and unsupervised techniques as well as advanced techniques such as data scraping and text management. Case studies are featured throughout along with examples for each technique presented. R code and solutions to exercises featured in the book are provided on a supporting website.
(source: Nielsen Book Data)

• Foreword
• Editors
• Contributors
• 1 Empowering Clinician-Scientists in the Information Age of Omics and Data Science / PAMELA A. TAMEZ AND MARY B. ENGLER
• 2 Making Data Matter: Identifying Care Opportunities for US Healthcare Transformation / MARKA. CARON
• 3 Turning Data into Enhanced Value for Patients / KYUN HEE (KEN) LEE
• 4 Data Analytics for the Clinical Researcher / MINJAE KIM
• 5 Intelligent Healthcare: The Case of the Emergency Department / SHIVARAM POIGAI ARUNACHALAM, MUSTAFA SIR, AND KALYAN S. PASUPATHY
• 6 Network Analytics to Enable Decisions in Healthcare Management / UMA SRINIVASAN, ARIF KHAN, AND SHAHADAT UDDIN
• 7 Modeling and Analysis of Behavioral Health Data Using Graph Analytics / ROSE YESHA AND ARYYA GANGOPADHYAY
• 8 The Heart of the Digital Workplace: Intelligent Search Moves the Measure from Efficiency to Proficiency for a Fortune 50 Healthcare Company / JAY LIEBOWITZ AND DIANE BERRY
• 9 The Promise of Big Data Analytics Transcending K no ledge
• Discovery through Point-of-Care Applications / LAVI OUD
• 10 Predictive Analytics and Mac hine Learning in Medicine / L. NELSON SANCHEZ-PINTO AND MATTHEW M. CHURPEK
• 11 High-Dimensional Models and Analytics in Large Database Applications / MICHAEL BRIMACOMBE
• 12 Learning to Extract Actionable Evidence from Medical Insurance Claims Data / JIESHI CHEN AND ARTUR DUBRAWSKI
• 13 The Role of Unstructured Data in Healthcare Analytics 41 / AMANDA DAWSON AND SERGEI ANANYAN
• Index.

This book shows healthcare professionals how to turn data points into meaningful knowledge upon which they can take effective action. Actionable intelligence can take many forms, from informing health policymakers on e ective strategies for the population to providing direct and predictive insights on patients to healthcare providers so they can achieve positive outcomes. It can assist those performing clinical research where relevant statistical methods are applied to both identify the efficacy of treatments and improve clinical trial design. It also benefits healthcare data standards groups through which pertinent data governance policies are implemented to ensure quality data are obtained, measured, and evaluated for the benefit of all involved. Although the obvious constant thread among all of these important healthcare use cases of actionable intelligence is the data at hand, such data in and of itself merely represents one element of the full structure of healthcare data analytics. This book examines the structure for turning data into actionable knowledge and discusses: * The importance of establishing research questions * Data collection policies and data governance * Principle-centered data analytics to transform data into information * Understanding the "why" of classified causes and effects * Narratives and visualizations to inform all interested parties Actionable Intelligence in Healthcare is an important examination of how proper healthcare-related questions should be formulated, how relevant data must be transformed to associated information, and how the processing of information relates to knowledge. It indicates to clinicians and researchers why this relative knowledge is meaningful and how best to apply such newfound understanding for the betterment of all.
(source: Nielsen Book Data)

• Preface ix
• List of Figures xi
• 1. Introduction 1
• 2. Activities 5
• 2.1 Definitions 5
• 2.2 Classes of Activities 7
• 2.3 Additional Reading 8
• 3. Sensing 11
• 3.1 Sensors Used for Activity Learning 11
• 3.1.1 Sensors in the Environment 12
• 3.1.2 Sensors on the Body 15
• 3.2 Sample Sensor Datasets 17
• 3.3 Features 17
• 3.3.1 Sequence Features 21
• 3.3.2 Discrete Event Features 23
• 3.3.3 Statistical Features 25
• 3.3.4 Spectral Features 31
• 3.3.5 Activity Context Features 34
• 3.4 Multisensor Fusion 34
• 3.5 Additional Reading 38
• 4. Machine Learning 41
• 4.1 Supervised Learning Framework 41
• 4.2 Naive Bayes Classifier 44
• 4.3 Gaussian Mixture Model 48
• 4.4 Hidden Markov Model 50
• 4.5 Decision Tree 54
• 4.6 Support Vector Machine 56
• 4.7 Conditional Random Field 62
• 4.8 Combining Classifier Models 63
• 4.8.1 Boosting 64
• 4.8.2 Bagging 65
• 4.9 Dimensionality Reduction 66
• 4.10 Additional Reading 72
• 5. Activity Recognition 75
• 5.1 Activity Segmentation 76
• 5.2 Sliding Windows 81
• 5.2.1 Time Based Windowing 81
• 5.2.2 Size Based Windowing 82
• 5.2.3 Weighting Events Within a Window 83
• 5.2.4 Dynamic Window Sizes 87
• 5.3 Unsupervised Segmentation 88
• 5.4 Measuring Performance 92
• 5.4.1 Classifier-Based Activity Recognition Performance Metrics 95
• 5.4.2 Event-Based Activity Recognition Performance Metrics 99
• 5.4.3 Experimental Frameworks for Evaluating Activity Recognition 102
• 5.5 Additional Reading 103
• 6. Activity Discovery 107
• 6.1 Zero-Shot Learning 108
• 6.2 Sequence Mining 110
• 6.2.1 Frequency-Based Sequence Mining 111
• 6.2.2 Compression-Based Sequence Mining 112
• 6.3 Clustering 117
• 6.4 Topic Models 119
• 6.5 Measuring Performance 121
• 6.5.1 Expert Evaluation 121
• 6.6 Additional Reading 124
• 7. Activity Prediction 127
• 7.1 Activity Sequence Prediction 128
• 7.2 Activity Forecasting 133
• 7.3 Probabilistic Graph-Based Activity Prediction 137
• 7.4 Rule-Based Activity Timing Prediction 139
• 7.5 Measuring Performance 142
• 7.6 Additional Reading 146
• 8. Activity Learning in the Wild 149
• 8.1 Collecting Annotated Sensor Data 149
• 8.2 Transfer Learning 158
• 8.2.1 Instance and Label Transfer 162
• 8.2.2 Feature Transfer with No Co-occurrence Data 166
• 8.2.3 Informed Feature Transfer with Co-occurrence Data 167
• 8.2.4 Uninformed Feature Transfer with Co-occurrence Data Using a Teacher Learner Model 168
• 8.2.5 Uninformed Feature Transfer with Co-occurrence Data Using Feature Space Alignment 170
• 8.3 Multi-Label Learning 170
• 8.3.1 Problem Transformation 173
• 8.3.2 Label Dependency Exploitation 174
• 8.3.3 Evaluating the Performance of Multi-Label Learning Algorithms 179
• 8.4 Activity Learning for Multiple Individuals 180
• 8.4.1 Learning Group Activities 180
• 8.4.2 Train on One/Test on Multiple 183
• 8.4.3 Separating Event Streams 185
• 8.4.4 Tracking Multiple Users 188
• 8.5 Additional Reading 190
• 9. Applications of Activity Learning 195
• 9.1 Health 195
• 9.2 Activity-Aware Services 198
• 9.3 Security and Emergency Management 199
• 9.4 Activity Reconstruction, Expression and Visualization 201
• 9.5 Analyzing Human Dynamics 207
• 9.6 Additional Reading 210
• 10. The Future of Activity Learning 213
• Appendix: Sample Activity Data 217
• Bibliography 237
• Index 253.
• (source: Nielsen Book Data)

Defines the notion of an activity model learned from sensor data and presents key algorithms that form the core of the field Activity Learning: Discovering, Recognizing and Predicting Human Behavior from Sensor Data provides an in-depth look at computational approaches to activity learning from sensor data. Each chapter is constructed to provide practical, step-by-step information on how to analyze and process sensor data. The book discusses techniques for activity learning that include the following: Discovering activity patterns that emerge from behavior-based sensor data Recognizing occurrences of predefined or discovered activities in real time Predicting the occurrences of activities The techniques covered can be applied to numerous fields, including security, telecommunications, healthcare, smart grids, and home automation. An online companion site enables readers to experiment with the techniques described in the book, and to adapt or enhance the techniques for their own use. With an emphasis on computational approaches, Activity Learning: Discovering, Recognizing, and Predicting Human Behavior from Sensor Data provides graduate students and researchers with an algorithmic perspective to activity learning.
(source: Nielsen Book Data)

• PREFACE ix ACKNOWLEDGMENTS xiii CONTRIBUTORS xv I PRELIMINARIES
• 1 A Brief Introduction to Evolutionary and other Nature-Inspired Algorithms 3 Nasimul Noman and Hitoshi Iba
• 2 Mathematical Models and Computational Methods for Inference of Genetic Networks 30 Tatsuya Akutsu
• 3 Gene Regulatory Networks: Real Data Sources and Their Analysis 49 Yuji Zhang II EAs FOR GENE EXPRESSION DATA ANALYSIS AND GRN RECONSTRUCTION
• 4 Biclustering Analysis of Gene Expression Data Using Evolutionary Algorithms 69 Alan Wee-Chung Liew
• 5 Inference of Vohradsk' y s Models of Genetic Networks Using a Real-Coded Genetic Algorithm 96 Shuhei Kimura
• 6 GPU-Powered Evolutionary Design of Mass-Action-Based Models of Gene Regulation 118 Marco S. Nobile, Davide Cipolla, Paolo Cazzaniga and Daniela Besozzi
• 7 Modeling Dynamic Gene Expression in Streptomyces Coelicolor: Comparing Single and Multi-Objective Setups 151 Spencer Angus Thomas, Yaochu Jin, Emma Laing and Colin Smith
• 8 Reconstruction of Large-Scale Gene Regulatory Network Using S-system Model 185 Ahsan Raja Chowdhury and Madhu Chetty III EAs FOR EVOLVING GRNs AND REACTION NETWORKS
• 9 Design Automation of Nucleic Acid Reaction System Simulated by Chemical Kinetics Based on Graph Rewriting Model 213 Ibuki Kawamata and Masami Hagiya
• 10 Using Evolutionary Algorithms to Study the Evolution of Gene Regulatory Networks Controlling Biological Development 240 Alexander Spirov and David Holloway
• 11 Evolving GRN-inspired In Vitro Oscillatory Systems 269 Quang Huy Dinh, Nathanael Aubert, Nasimul Noman, Hitoshi Iba and Yannic Rondelez IV APPLICATION OF GRN WITH EAs
• 12 Artificial Gene Regulatory Networks for Agent Control 301 Sylvain Cussat-Blanc, Jean Disset, St'ephane Sanchez and Yves Duthen
• 13 Evolving H-GRNs for Morphogenetic Adaptive Pattern Formation of Swarm Robots 327 Hyondong Oh and Yaochu Jin
• 14 Regulatory Representations in Architectural Design 362 Daniel Richards and Martyn Amos
• 15 Computing with Artificial Gene Regulatory Networks 398 Michael A. Lones INDEX.
• (source: Nielsen Book Data)

Introducing a handbook for gene regulatory network research using evolutionary computation, with applications for computer scientists, computational and system biologists This book is a step-by-step guideline for research in gene regulatory networks (GRN) using evolutionary computation (EC). The book is organized into four parts that deliver materials in a way equally attractive for a reader with training in computation or biology. Each of these sections, authored by well-known researchers and experienced practitioners, provides the relevant materials for the interested readers. The first part of this book contains an introductory background to the field. The second part presents the EC approaches for analysis and reconstruction of GRN from gene expression data. The third part of this book covers the contemporary advancements in the automatic construction of gene regulatory and reaction networks and gives direction and guidelines for future research. Finally, the last part of this book focuses on applications of GRNs with EC in other fields, such as design, engineering and robotics. Provides a reference for current and future research in gene regulatory networks (GRN) using evolutionary computation (EC) Covers sub-domains of GRN research using EC, such as expression profile analysis, reverse engineering, GRN evolution, applications Contains useful contents for courses in gene regulatory networks, systems biology, computational biology, and synthetic biology Delivers state-of-the-art research in genetic algorithms, genetic programming, and swarm intelligence Evolutionary Computation in Gene Regulatory Network Research is a reference for researchers and professionals in computer science, systems biology, and bioinformatics, as well as upper undergraduate, graduate, and postgraduate students. Hitoshi Iba is a Professor in the Department of Information and Communication Engineering, Graduate School of Information Science and Technology, at the University of Tokyo, Toyko, Japan. He is an Associate Editor of the IEEE Transactions on Evolutionary Computation and the journal of Genetic Programming and Evolvable Machines. Nasimul Noman is a lecturer in the School of Electrical Engineering and Computer Science at the University of Newcastle, NSW, Australia. From 2002 to 2012 he was a faculty member at the University of Dhaka, Bangladesh. Noman is an Editor of the BioMed Research International journal. His research interests include computational biology, synthetic biology, and bioinformatics.
(source: Nielsen Book Data)

• Preface xv Acknowledgements xxi 1 Fundamentals of Pattern Recognition 1 1.1 Basic Concepts: Class Feature Data Set 1 1.1.1 Classes and Class Labels 1 1.1.2 Features 2 1.1.3 Data Set 3 1.1.4 Generate Your Own Data 6 1.2 Classifier Discriminant Functions Classification Regions 9 1.3 Classification Error and Classification Accuracy 11 1.3.1 Where Does the Error Come From? Bias and Variance 11 1.3.2 Estimation of the Error 13 1.3.3 Confusion Matrices and Loss Matrices 14 1.3.4 Training and Testing Protocols 15 1.3.5 Overtraining and Peeking 17 1.4 Experimental Comparison of Classifiers 19 1.4.1 Two Trained Classifiers and a Fixed Testing Set 20 1.4.2 Two Classifier Models and a Single Data Set 22 1.4.3 Two Classifier Models and Multiple Data Sets 26 1.4.4 Multiple Classifier Models and Multiple Data Sets 27 1.5 Bayes Decision Theory 30 1.5.1 Probabilistic Framework 301.5.2 Discriminant Functions and Decision Boundaries 31 1.5.3 Bayes Error 33 1.6 Clustering and Feature Selection 35 1.6.1 Clustering 35 1.6.2 Feature Selection 37 1.7 Challenges of Real-Life Data 40
• Appendix 41 1.A.1 Data Generation 41 1.A.2 Comparison of Classifiers 42 1.A.2.1 MATLAB Functions for Comparing Classifiers 42 1.A.2.2 Critical Values for Wilcoxon and Sign Test 45 1.A.3 Feature Selection 47 2 Base Classifiers 49 2.1 Linear and Quadratic Classifiers 49 2.1.1 Linear Discriminant Classifier 49 2.1.2 Nearest Mean Classifier 52 2.1.3 Quadratic Discriminant Classifier 52 2.1.4 Stability of LDC and QDC 53 2.2 Decision Tree Classifiers 55 2.2.1 Basics and Terminology 55 2.2.2 Training of Decision Tree Classifiers 57 2.2.3 Selection of the Feature for a Node 58 2.2.4 Stopping Criterion 60 2.2.5 Pruning of the Decision Tree 63 2.2.6 C4.5 and ID3 64 2.2.7 Instability of Decision Trees 64 2.2.8 Random Trees 65 2.3 The Nayve Bayes Classifier 66 2.4 Neural Networks 68 2.4.1 Neurons 68 2.4.2 Rosenblatt s Perceptron 70 2.4.3 Multi-Layer Perceptron 71 2.5 Support Vector Machines 73 2.5.1 Why Would It Work? 73 2.5.2 Classification Margins 74 2.5.3 Optimal Linear Boundary 76 2.5.4 Parameters and Classification Boundaries of SVM 78 2.6 The k-Nearest Neighbor Classifier (k-nn) 80 2.7 Final Remarks 82 2.7.1 Simple or Complex Models? 82 2.7.2 The Triangle Diagram 83 2.7.3 Choosing a Base Classifier for Ensembles 85
• Appendix 85 2.A.1 MATLAB Code for the Fish Data 85 2.A.2 MATLAB Code for Individual Classifiers 86 2.A.2.1 Decision Tree 86 2.A.2.2 Nayve Bayes 89 2.A.2.3 Multi-Layer Perceptron 90 2.A.2.4 1-nn Classifier 92 3 An Overview of the Field 94 3.1 Philosophy 94 3.2 Two Examples 98 3.2.1 The Wisdom of the Classifier Crowd 98 3.2.2 The Power of Divide-and-Conquer 98 3.3 Structure of the Area 100 3.3.1 Terminology 100 3.3.2 A Taxonomy of Classifier Ensemble Methods 100 3.3.3 Classifier Fusion and Classifier Selection 104 3.4 Quo Vadis? 105 3.4.1 Reinventing the Wheel? 105 3.4.2 The Illusion of Progress? 106 3.4.3 A Bibliometric Snapshot 107 4 Combining Label Outputs 111 4.1 Types of Classifier Outputs 111 4.2 A Probabilistic Framework for Combining Label Outputs 112 4.3 Majority Vote 113 4.3.1 Democracy in Classifier Combination 113 4.3.2 Accuracy of the Majority Vote 114 4.3.3 Limits on the Majority Vote Accuracy: An Example 117 4.3.4 Patterns of Success and Failure 119 4.3.5 Optimality of the Majority Vote Combiner 124 4.4 Weighted Majority Vote 125 4.4.1 Two Examples 126 4.4.2 Optimality of the Weighted Majority Vote Combiner 127 4.5 Nayve-Bayes Combiner 128 4.5.1 Optimality of the Nayve Bayes Combiner 128 4.5.2 Implementation of the NB Combiner 130 4.6 Multinomial Methods 132 4.7 Comparison of Combination Methods for Label Outputs 135
• Appendix 137 4.A.1 Matan s Proof for the Limits on the Majority Vote Accuracy 137 4.A.2 Selected MATLAB Code 139 5 Combining Continuous-Valued Outputs 143 5.1 Decision Profile 143 5.2 How Do We Get Probability Outputs? 144 5.2.1 Probabilities Based on Discriminant Scores 144 5.2.2 Probabilities Based on Counts: Laplace Estimator 147 5.3 Nontrainable (Fixed) Combination Rules 150 5.3.1 A Generic Formulation 150 5.3.2 Equivalence of Simple Combination Rules 152 5.3.3 Generalized Mean Combiner 153 5.3.4 A Theoretical Comparison of Simple Combiners 156 5.3.5 Where Do They Come From? 160 5.4 The Weighted Average (Linear Combiner) 166 5.4.1 Consensus Theory 166 5.4.2 Added Error for the Weighted Mean Combination 167 5.4.3 Linear Regression 168 5.5 A Classifier as a Combiner 172 5.5.1 The Supra Bayesian Approach 172 5.5.2 Decision Templates 173 5.5.3 A Linear Classifier 175 5.6 An Example of Nine Combiners for Continuous-Valued Outputs 175 5.7 To Train or Not to Train? 176
• Appendix 178 5.A.1 Theoretical Classification Error for the Simple Combiners 178 5.A.1.1 Set-up and Assumptions 178 5.A.1.2 Individual Error 180 5.A.1.3 Minimum and Maximum 180 5.A.1.4 Average (Sum) 181 5.A.1.5 Median and Majority Vote 182 5.A.1.6 Oracle 183 5.A.2 Selected MATLAB Code 183 6 Ensemble Methods 186 6.1 Bagging 186 6.1.1 The Origins: Bagging Predictors 186 6.1.2 Why Does Bagging Work? 187 6.1.3 Out-of-bag Estimates 189 6.1.4 Variants of Bagging 190 6.2 Random Forests 190 6.3 AdaBoost 192 6.3.1 The AdaBoost Algorithm 192 6.3.2 The arc-x4 Algorithm 194 6.3.3 Why Does AdaBoost Work? 195 6.3.4 Variants of Boosting 199 6.3.5 A Famous Application: AdaBoost for Face Detection 199 6.4 Random Subspace Ensembles 203 6.5 Rotation Forest 204 6.6 Random Linear Oracle 208 6.7 Error Correcting Output Codes (ECOC) 211 6.7.1 Code Designs 212 6.7.2 Decoding 214 6.7.3 Ensembles of Nested Dichotomies 216
• Appendix 218 6.A.1 Bagging 218 6.A.2 AdaBoost 220 6.A.3 Random Subspace 223 6.A.4 Rotation Forest 225 6.A.5 Random Linear Oracle 228 6.A.6 ECOC 229 7 Classifier Selection 230 7.1 Preliminaries 230 7.2 Why Classifier Selection Works 231 7.3 Estimating Local Competence Dynamically 233 7.3.1 Decision-Independent Estimates 233 7.3.2 Decision-Dependent Estimates 238 7.4 Pre-Estimation of the Competence Regions 239 7.4.1 Bespoke Classifiers 240 7.4.2 Clustering and Selection 241 7.5 Simultaneous Training of Regions and Classifiers 242 7.6 Cascade Classifiers 244 Appendix: Selected MATLAB Code 244 7.A.1 Banana Data 244 7.A.2 Evolutionary Algorithm for a Selection Ensemble for the Banana Data 245 8 Diversity in Classifier Ensembles 247 8.1 What Is Diversity? 247 8.1.1 Diversity for a Point-Value Estimate 248 8.1.2 Diversity in Software Engineering 248 8.1.3 Statistical Measures of Relationship 249 8.2 Measuring Diversity in Classifier Ensembles 250 8.2.1 Pairwise Measures 250 8.2.2 Nonpairwise Measures 251 8.3 Relationship Between Diversity and Accuracy 256 8.3.1 An Example 256 8.3.2 Relationship Patterns 258 8.3.3 A Caveat: Independent Outputs Independent Errors 262 8.3.4 Independence Is Not the Best Scenario 265 8.3.5 Diversity and Ensemble Margins 267 8.4 Using Diversity 270 8.4.1 Diversity for Finding Bounds and Theoretical Relationships 270 8.4.2 Kappa-error Diagrams and Ensemble Maps 271 8.4.3 Overproduce and Select 275 8.5 Conclusions: Diversity of Diversity 279
• Appendix 280 8.A.1 Derivation of Diversity Measures for Oracle Outputs 280 8.A.1.1 Correlation 280 8.A.1.2 Interrater Agreement 281 8.A.2 Diversity Measure Equivalence 282 8.A.3 Independent Outputs Independent Errors 284 8.A.4 A Bound on the Kappa-Error Diagram 286 8.A.5 Calculation of the Pareto Frontier 287 9 Ensemble Feature Selection 290 9.1 Preliminaries 290 9.1.1 Right and Wrong Protocols 290 9.1.2 Ensemble Feature Selection Approaches 294 9.1.3 Natural Grouping 294 9.2 Ranking by Decision Tree Ensembles 295 9.2.1 Simple Count and Split Criterion 295 9.2.2 Permuted Features or the Noised-up Method 297 9.3 Ensembles of Rankers 299 9.3.1 The Approach 299 9.3.2 Ranking Methods (Criteria) 300 9.4 Random Feature Selection for the Ensemble 305 9.4.1 Random Subspace Revisited 305 9.4.2 Usability Coverage and Feature Diversity 306 9.4.3 Genetic Algorithms 312 9.5 Nonrandom Selection 315 9.5.1 The Favorite Class Model 315 9.5.2 The Iterative Model 315 9.5.3 The Incremental Model 316 9.6 A Stability Index 317 9.6.1 Consistency Between a Pair of Subsets 317 9.6.2 A Stability Index for K Sequences 319 9.6.3 An Example of Applying the Stability Index 320
• Appendix 322 9.A.1 MATLAB Code for the Numerical Example of Ensemble Ranking 322 9.A.2 MATLAB GA Nuggets 322 9.A.3 MATLAB Code for the Stability Index 324 10 A Final Thought 326 References 327 Index 353.
• (source: Nielsen Book Data)

A unified, coherent treatment of current classifier ensemble methods, from fundamentals of pattern recognition to ensemble feature selection, now in its second edition The art and science of combining pattern classifiers has flourished into a prolific discipline since the first edition of Combining Pattern Classifiers was published in 2004. Dr. Kuncheva has plucked from the rich landscape of recent classifier ensemble literature the topics, methods, and algorithms that will guide the reader toward a deeper understanding of the fundamentals, design, and applications of classifier ensemble methods. Thoroughly updated, with MATLAB(R) code and practice data sets throughout, Combining Pattern Classifiers includes: * Coverage of Bayes decision theory and experimental comparison of classifiers * Essential ensemble methods such as Bagging, Random forest, AdaBoost, Random subspace, Rotation forest, Random oracle, and Error Correcting Output Code, among others * Chapters on classifier selection, diversity, and ensemble feature selection With firm grounding in the fundamentals of pattern recognition, and featuring more than 140 illustrations, Combining Pattern Classifiers, Second Edition is a valuable reference for postgraduate students, researchers, and practitioners in computing and engineering.
(source: Nielsen Book Data)

• Preface xv Acknowledgments xvii About the Author xix
• PART 1 THINK 1
• Chapter 1 Introduction 3 Pain Points 4 Birth of a Start-up 5 Experience Is the Teacher of All Things 7 #healthcaretrends 10
• Chapter 2 Insight 15 The Experience Blueprint 17 Dennis s Story 20 Defining My Criteria 23
• Chapter 3 Inspiration 29 Learning from Banking 31 Learning from Retail 32 Learning from Healthcare 34 But How Do They Do It? 35
• Chapter 4 Ideation 39 The Fun
• Part 40 Healthcare 2020 40 Patient Empowerment 42 The Personal Health Cloud 45
• PART 2 DO 49
• Chapter 5 Implementation
• Part 1 51 From Ideas to Reality 52 Technology Feasibility 53 Big Data 55 Analytics 59 Trends Impacting Advanced Analytics 61 Kicking It Up a Notch 74 Behavioral Analytics 75 Personal Health Analytics 77 Visual Analytics 79
• Chapter 6 Implementation
• Part 2 85 Back to the Blueprint 86 Changing Behavior 86 Tools for Providers and Payers 87 Population Health Analytics 88 Consumer Tools 91 Consumer Choice and Transparency 97 Where Will Transparency Lead Us? 99 What Is an APCD? 101 What s the Landscape? 102 The Key to Success: Big Data Analytics 107 Establishing an APCD 108 Why APCDs? 112 Clinical Trial Data Transparency 113
• Chapter 7 Implementation
• Part 3 117 Why Volume to Value? 118 The Shifting Incentive and Its Adoption 119 Why Episode Analytics? 121 Constructing an Episode of Care 122 What Constitutes an Episode of Care? 123 Calculating the Cost of an Episode 124 Patient and Population: Analyzing Costs and Outcomes 125 The Holistic View of a Patient 130 Provider Performance 130 Diagnosis and Personalization 132 Machine Learning and AI 134 Natural Language Processing 136
• Chapter 8 Innovation 141 Putting It All Together 142 Design Thinking Tools 143 Exponential Growth 145 Modernization 148 Traditional Modern 152 Interoperability Roadmap 153
• Chapter 9 Individual 157 Where do we go from here? 159 Build for How the Healthcare System Should Work 162 Rethink Workflows and Experiences 163 Develop Things That Work Together 165 Close the Loop 166 Final Thoughts 166 Appendix Chapter Summaries 169
• Part 1: Think 170
• Part 2: Do 176 Index 187.
• (source: Nielsen Book Data)

In The Patient Revolution, author Krisa Tailor a noted expert in health care innovation and management explores, through the lens of design thinking, how information technology will take health care into the experience economy. In the experience economy, patients will shift to being empowered consumers who are active participants in their own care. Tailor explores this shift by creating a vision for a newly designed health care system that's focused on both sickness and wellness, and is driven by data and analytics. The new system seamlessly integrates health into our daily lives, and delivers care so uniquely personalized that no two people are provided identical treatments. Connected through data, everyone across the health care ecosystem, including clinicians, insurers, and researchers, will be able to meet individuals wherever they are in their health journey to reach the ultimate goal of keeping people healthy. The patient revolution has just begun and an exciting journey awaits us. Praise for the patient revolution "A full 50% of the US population has at least one chronic disease that requires ongoing monitoring and treatment. Our current health care system is woefully inadequate in providing these individuals with the treatment and support they need. This disparity can only be addressed through empowering patients to better care for themselves and giving providers better tools to care for their patients. Both of those solutions will require the development and application of novel technologies. In Krisa Tailor's book The Patient Revolution, a blueprint is articulated for how this could be achieved, culminating in a vision for a learning health system within 10 years." Ricky Bloomfield, MD, Director, Mobile Technology Strategy; Assistant Professor, Duke Medicine "In The Patient Revolution, Krisa Tailor astutely points out that 80% of health is impacted by factors outside of the health care system. Amazon unfortunately knows more about our patients than we do. The prescriptive analytics she describes will allow health care providers to use big data to optimize interventions at the level of the individual patient. The use of analytics will allow providers to improve quality, shape care coordination, and contain costs. Advanced analytics will lead to personalized care and ultimately empowered patients!" Linda Butler, MD, Vice President of Medical Affairs/Chief Medical Officer/Chief Medical Information Officer, Rex Healthcare "The Patient Revolution provides a practical roadmap on how the industry can capture value by making health and care more personalized, anticipatory, and intuitive to patient needs." Ash Damle, CEO, Lumiata "Excellent read. For me, health care represents a unique economy one focused on technology, but requiring a deep understanding of humanity. Ms. Tailor begins the exploration of how we provide care via the concepts of design thinking, asking how we might redesign care with an eye toward changing the experience. She does an excellent job deconstructing this from the patient experience. I look forward to a hopeful follow-up directed at changing the provider culture." Alan Pitt, MD, Chief Medical Officer, Avizia "Whether you're a health care provider looking to gain an understanding of the health care landscape, a health data scientist, or a seasoned business pro, you'll come away with a deeper, nuanced understanding of today's evolving health care system with this book. Krisa Tailor ties together in a comprehensive, unique way the worlds of health care administration, clinical practice, design thinking, and business strategy and innovation." Steven Chan, MD, MBA, University of California, Davis.
(source: Nielsen Book Data)

• 1. An Artificial Intelligence System Based Power Estimation Method for CMOS VLSI Circuits
• [Govindaraj Vellingiri and Ramesh Jayabalan]
• 2. Awareness Alert and Information Analysis in Social Media Networking Using Usage Analysis and Negotiable Approach
• [Alamelu Muthukrishnan]
• 3. Object Detection and Tracking in Video Using Deep Learning Techniques: A Review
• [Dhivya Praba Ramasamy and Kavitha Kanagaraj]
• 4. Fuzzy MCDM: Application in Disease Risk and Prediction
• [Rachna Jain, Meenu Gupta, Abhishek Kathuria, and Devanshi Mukhopadhyay]
• 5. Deep Learning Approach to Predict and Grade Glaucoma from Fundus Images through Constitutional Neural Networks
• [Kishore Balasubramanian and N. B. Ananthamoorthy]
• 6. A Novel Method for Securing Cognitive Radio Communication Network Using the Machine Learning Schemes and a Rule Based Approaches
• [Antony Hyils Sharon Magdalene and Lakshmanan Thulasimani]
• 7. Detection of Retinopathy of Prematurity Using Convolution Neural Network
• [Deepa Dhanaskodi and Poongodi Chenniappan]
• 8. Impact of Technology on Human Resource Information System and Achieving Business Intelligence in Organizations
• [Sharanika Dhal, Manas Kumar Pal, Archana Choudhary, and Mamata Rath]
• 9. Proficient Prediction of Acute Lymphoblastic Leukemia Using Machine Learning Algorithm
• [M. Sangeetha, K.N. Apinaya Prethi, and S. Nithya]
• 10. Role of Machine Learning in Social Area Networks
• [Rajeswari Arumugam, Premalatha Balasubramaniam, and Cynthia Joseph]
• 11. Breast Cancer and Machine Learning: Interactive Breast Cancer Prediction Using Naive Bayes Algorithm
• [Atapaka Thrilok Gayathri and Samuel Theodore Deepa]
• 12. Deep Networks and Deep Learning Algorithms
• [Tannu Kumari and Anjana Mishra]
• 13. Machine Learning for Big Data Analytics, Interactive and Reinforcement
• [Ritwik Raj and Anjana Mishra]
• 14. Fish Farm Monitoring System Using IoT and Machine Learning
• [Farjana Yeasmin Trisha, Mohammad Farhan Ferdous, and Mahmudul Hasan].
• (source: Nielsen Book Data)

Artificial Intelligence (AI), when incorporated with machine learning and deep learning algorithms, has a wide variety of applications today. This book focuses on the implementation of various elementary and advanced approaches in AI that can be used in various domains to solve real-time decision-making problems. The book focuses on concepts and techniques used to run tasks in an automated manner. It discusses computational intelligence in the detection and diagnosis of clinical and biomedical images, covers the automation of a system through machine learning and deep learning approaches, presents data analytics and mining for decision-support applications, and includes case-based reasoning, natural language processing, computer vision, and AI approaches in real-time applications. Academic scientists, researchers, and students in the various domains of computer science engineering, electronics and communication engineering, and information technology, as well as industrial engineers, biomedical engineers, and management, will find this book useful. By the end of this book, you will understand the fundamentals of AI. Various case studies will develop your adaptive thinking to solve real-time AI problems. Features Includes AI-based decision-making approaches Discusses computational intelligence in the detection and diagnosis of clinical and biomedical images Covers automation of systems through machine learning and deep learning approaches and its implications to the real world Presents data analytics and mining for decision-support applications Offers case-based reasoning.
(source: Nielsen Book Data)

• I: STARTERSIntroductionThe Computational Problem Solving CycleExample: Simple Knapsack ModelsAn Example: The Eilon Simple Knapsack ModelScoping Out Post-Solution AnalysisParameter Sweeping: A Method for Post-Solution AnalysisDecision SweepingSummary of Vocabulary and Main PointsFor ExplorationFor More InformationConstrained Optimization Models: Introduction and Concepts Constrained OptimizationClassification of ModelsSolution ConceptsComputational Complexity and Solution MethodsMetaheuristicsDiscussionFor ExplorationFor More InformationLinear Programming IntroductionWagner Diet ProblemSolving an LPPost-Solution Analysis of LPsMore than One at a Time: The 100% RuleFor ExplorationFor More InformationII: OPTIMIZATION MODELING Simple Knapsack Problems IntroductionSolving a Simple Knapsack in ExcelThe Bang-for-Buck HeuristicPost-Solution Analytics with the Simple KnapsackCreating Simple Knapsack Test ModelsDiscussionFor ExplorationFor More InformationAssignment ProblemsIntroductionThe Generalized Assignment ProblemCase Example: GAP 1-c5-15-1Using Decisions from Evolutionary ComputationDiscussionFor ExplorationFor More InformationThe Traveling Salesman ProblemIntroductionProblem DefinitionSolution ApproachesDiscussionFor ExplorationFor More InformationVehicle Routing ProblemsIntroductionProblem DefinitionSolution ApproachesExtensions of VRPFor ExplorationFor More InformationResource-Constrained Scheduling IntroductionFormal DefinitionSolution ApproachesExtensions of RCPSPFor ExplorationFor More InformationLocation Analysis IntroductionLocating One Service CenterA Nave Greedy Heuristic for Locating n CentersUsing a Greedy Hill Climbing HeuristicDiscussionFor ExplorationFor More InformationTwo-Sided Matching Quick Introduction: Two-Sided Matching ProblemsNarrative Description of Two-Sided Matching ProblemsRepresenting the ProblemStable Matches and the Deferred Acceptance AlgorithmOnce More, in More DepthGeneralization: Matching in Centralized MarketsDiscussion: ComplicationsFor More InformationIII: METAHEURISTIC SOLUTION METHODSLocal Search Metaheuristics IntroductionGreedy Hill ClimbingSimulated AnnealingRunning the Simulated Annealer CodeThreshold Accepting AlgorithmsTabu SearchFor ExplorationFor More InformationEvolutionary AlgorithmsIntroductionEPs: Evolutionary ProgramsThe Basic Genetic Algorithm (GA)For ExplorationFor More InformationIdentifying and Collecting Decisions of Interest Kinds of Decisions of Interest (DoIs)The FI2-Pop GADiscussionFor ExplorationFor More InformationIV: POST-SOLUTION ANALYSIS OF OPTIMIZATION MODELSDecision Sweeping IntroductionDecision Sweeping with the GAP 1-c5-15-1 ModelDeliberating with the Results of a Decision SweepDiscussionFor ExplorationFor More InformationParameter Sweeping Introduction: Reminders on Solution Pluralism and Parameter SweepingParameter Sweeping: Post-Solution Analysis by Model Re-SolutionParameter Sweeping with Decision SweepingDiscussionFor ExplorationFor More InformationMultiattribute Utility Modeling IntroductionSingle Attribute Utility ModelingMultiattribute Utility ModelsDiscussionFor ExplorationFor More InformationData Envelopment Analysis IntroductionImplementationDemonstration of DEA ConceptDiscussionFor ExplorationFor More InformationRedistricting: A Case Study in Zone Design IntroductionThe Basic Redistricting FormulationRepresenting and Formulating the ProblemInitial Forays for Discovering Good Districting PlansSolving a Related Solution Pluralism ProblemDiscussionFor ExplorationFor More InformationV: CONCLUSIONConclusionLooking BackRevisiting Post-Solution AnalysisLooking ForwardResources A.1 Resources on the WebBibliography Index.

Business Analytics for Decision Making, the first complete text suitable for use in introductory Business Analytics courses, establishes a national syllabus for an emerging first course at an MBA or upper undergraduate level. This timely text is mainly about model analytics, particularly analytics for constrained optimization. It uses implementations that allow students to explore models and data for the sake of discovery, understanding, and decision making. Business analytics is about using data and models to solve various kinds of decision problems. There are three aspects for those who want to make the most of their analytics: encoding, solution design, and post-solution analysis. This textbook addresses all three. Emphasizing the use of constrained optimization models for decision making, the book concentrates on post-solution analysis of models. The text focuses on computationally challenging problems that commonly arise in business environments. Unique among business analytics texts, it emphasizes using heuristics for solving difficult optimization problems important in business practice by making best use of methods from Computer Science and Operations Research. Furthermore, case studies and examples illustrate the real-world applications of these methods. The authors supply examples in Excel (R), GAMS, MATLAB (R), and OPL. The metaheuristics code is also made available at the book's website in a documented library of Python modules, along with data and material for homework exercises. From the beginning, the authors emphasize analytics and de-emphasize representation and encoding so students will have plenty to sink their teeth into regardless of their computer programming experience.
(source: Nielsen Book Data)

• Introduction : hacking on Twitter data
• Microformats : semantic markup and common sense collide
• Mailboxes : oldies but goodies
• Twitter : friends, followers, and setwise operations
• Twitter : the tweet, the whole tweet, and nothing but the tweet
• LinkedIn : clustering your professional network for fun (and profit?)
• Google buzz : TF-IDF, cosine similarity, and collocations
• Blogs et al. : natural language processing (and beyond)
• Facebook : the all-in-one wonder
• The semantic web : a cocktail discussion.

Facebook, Twitter, and LinkedIn generate a tremendous amount of valuable social data, but how can you find out who's making connections with social media, what they're talking about, or where they're located? This concise and practical book shows you how to answer these questions and more. You'll learn how to combine social web data, analysis techniques, and visualization to help you find what you've been looking for in the social haystack, as well as useful information you didn't know existed. Each standalone chapter introduces techniques for mining data in different areas of the social Web, including blogs and email. All you need to get started is a programming background and a willingness to learn basic Python tools. * Get a straightforward synopsis of the social web landscape * Use adaptable scripts on GitHub to harvest data from social network APIs such as Twitter, Facebook, and LinkedIn * Learn how to employ easy-to-use Python tools to slice and dice the data you collect * Explore social connections in microformats with the XHTML Friends Network * Apply advanced mining techniques such as TF-IDF, cosine similarity, collocation analysis, document summarization, and clique detection * Build interactive visualizations with web technologies based upon HTML5 and JavaScript toolkits "Let Matthew Russell serve as your guide to working with social data sets old (email, blogs) and new (Twitter, LinkedIn, Facebook). Mining the Social Web is a natural successor to Programming Collective Intelligence: a practical, hands-on approach to hacking on data from the social Web with Python." --Jeff Hammerbacher, Chief Scientist, Cloudera "A rich, compact, useful, practical introduction to a galaxy of tools, techniques, and theories for exploring structured and unstructured data." --Alex Martelli, Senior Staff Engineer, Google.
(source: Nielsen Book Data)

• Frontmatter
• Preface
• Contents
• Committees for Pattern Recognition: Infeasible Systems of Linear Inequalities, Hyperplane Arrangements, and Realizable Oriented Matroids
• 1. Oriented Matroids, the Pattern Recognition Problem, and Tope Committees
• 2. Boolean Intervals
• 3. Dehn-Sommerville Type Relations
• 4. Farey Subsequences
• 5. Blocking Sets of Set Families, and Absolute Blocking Constructions in Posets
• 6. Committees of Set Families, and Relative Blocking Constructions in Posets
• 7. Layers of Tope Committees
• 8. Three-Tope Committees
• 9. Halfspaces, Convex Sets, and Tope Committees
• 10. Tope Committees and Reorientations of Oriented Matroids
• 11. Topes and Critical Committees
• 12. Critical Committees and Distance Signals
• 13. Symmetric Cycles in the Hypercube Graphs
• Bibliography
• List of Notation
• Index.

Pattern Recognition on Oriented Matroids covers a range of innovative problems in combinatorics, poset and graph theories, optimization, and number theory that constitute a far-reaching extension of the arsenal of committee methods in pattern recognition. The groundwork for the modern committee theory was laid in the mid-1960s, when it was shown that the familiar notion of solution to a feasible system of linear inequalities has ingenious analogues which can serve as collective solutions to infeasible systems. A hierarchy of dialects in the language of mathematics, for instance, open cones in the context of linear inequality systems, regions of hyperplane arrangements, and maximal covectors (or topes) of oriented matroids, provides an excellent opportunity to take a fresh look at the infeasible system of homogeneous strict linear inequalities - the standard working model for the contradictory two-class pattern recognition problem in its geometric setting. The universal language of oriented matroid theory considerably simplifies a structural and enumerative analysis of applied aspects of the infeasibility phenomenon. The present book is devoted to several selected topics in the emerging theory of pattern recognition on oriented matroids: the questions of existence and applicability of matroidal generalizations of committee decision rules and related graph-theoretic constructions to oriented matroids with very weak restrictions on their structural properties; a study (in which, in particular, interesting subsequences of the Farey sequence appear naturally) of the hierarchy of the corresponding tope committees; a description of the three-tope committees that are the most attractive approximation to the notion of solution to an infeasible system of linear constraints; an application of convexity in oriented matroids as well as blocker constructions in combinatorial optimization and in poset theory to enumerative problems on tope committees; an attempt to clarify how elementary changes (one-element reorientations) in an oriented matroid affect the family of its tope committees; a discrete Fourier analysis of the important family of critical tope committees through rank and distance relations in the tope poset and the tope graph; the characterization of a key combinatorial role played by the symmetric cycles in hypercube graphs. Contents Oriented Matroids, the Pattern Recognition Problem, and Tope Committees Boolean Intervals Dehn-Sommerville Type Relations Farey Subsequences Blocking Sets of Set Families, and Absolute Blocking Constructions in Posets Committees of Set Families, and Relative Blocking Constructions in Posets Layers of Tope Committees Three-Tope Committees Halfspaces, Convex Sets, and Tope Committees Tope Committees and Reorientations of Oriented Matroids Topes and Critical Committees Critical Committees and Distance Signals Symmetric Cycles in the Hypercube Graphs.
(source: Nielsen Book Data)