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• 1. Introduction
• 2. Applications of Data Science
• 3. Widely Used Techniques in Data Science Applications
• 4. Data Preprocessing
• 5. Classification
• 6. Clustering
• 7. Text Mining
• 8. Deep Learning
• 9. Frequent Pattern Mining
• 10. Regression Analysis
• 11. Data Science Programming Language
• 12. Practical Data Science with WEKA.

This textbook comprehensively covers both fundamental and advanced topics related to data science. Data science is an umbrella term that encompasses data analytics, data mining, machine learning, and several other related disciplines. The chapters of this book are organized into three parts: The first part (chapters 1 to 3) is a general introduction to data science. Starting from the basic concepts, the book will highlight the types of data, its use, its importance and issues that are normally faced in data analytics, followed by presentation of a wide range of applications and widely used techniques in data science. The second part, which has been updated and considerably extended compared to the first edition, is devoted to various techniques and tools applied in data science. Its chapters 4 to 10 detail data pre-processing, classification, clustering, text mining, deep learning, frequent pattern mining, and regression analysis. Eventually, the third part (chapters 11 and 12) present a brief introduction to Python and R, the two main data science programming languages, and shows in a completely new chapter practical data science in the WEKA (Waikato Environment for Knowledge Analysis), an open-source tool for performing different machine learning and data mining tasks. An appendix explaining the basic mathematical concepts of data science completes the book. This textbook is suitable for advanced undergraduate and graduate students as well as for industrial practitioners who carry out research in data science. They both will not only benefit from the comprehensive presentation of important topics, but also from the many application examples and the comprehensive list of further readings, which point to additional publications providing more in-depth research results or provide sources for a more detailed description of related topics. "This book delivers a systematic, carefully thoughtful material on Data Science." from the Foreword by Witold Pedrycz, U Alberta, Canada.

Today, enterprises have more data at their disposal than ever before. Yet relatively few companies can quickly and efficiently get data to the people who need it for critical insights and important decisions. In this insightful report, David Sweenor and Melissa Burroughs from Alteryx explain analytic democratization, a strategy that leading organizations are now using to manage data access, analytics platforms, and data-driven decision-making. By democratizing analytics, you can enable all people within your business to create and leverage data-driven insights quickly and effectively. This self-service approach frees IT and data science personnel to focus on high-value initiatives. You'll learn how empowering knowledge workers with analytic access will benefit your business through optimized processes, faster translation of needs into solutions, and greater innovation.

Do your product dashboards look funky? Are your quarterly reports stale? Is the dataset you're using broken or just plain wrong? These problems affect almost every team, yet they're usually addressed on an ad hoc basis and in a reactive manner. If you answered yes to any of the questions above, this book is for you. Many data engineering teams today face the "good pipelines, bad data" problem. It doesn't matter how advanced your data infrastructure is if the data you're piping is bad. In this book, Barr Moses, Lior Gavish, and Molly Vorwerck from the data reliability company Monte Carlo explain how to tackle data quality and trust at scale by leveraging best practices and technologies used by some of the world's most innovative companies. Build more trustworthy and reliable data pipelines Write scripts to make data checks and identify broken pipelines with data observability Program your own data quality monitors from scratch Develop and lead data quality initiatives at your company Generate a dashboard to highlight your company's key data assets Automate data lineage graphs across your data ecosystem Build anomaly detectors for your critical data assets.
(source: Nielsen Book Data)

• Chapter 1 Introduction
• 1.1 Knowledge Discovery
• 1.2 Main Challenges
• 1.3 Book Overview
• Chapter 2 Functional-dependency-based truth discovery for isomorphic data
• 2.1 Handling independent constraints
• 2.2 Handling inter-related constraints
• 2.3 Inter-source data aggregation
• 2.4 Update source weights
• Chapter 3 Denial-constraint-based truth discovery for isomorphic data
• Describe the truth discovery strategies for isomorphic data based on denial constraints
• 4.1 Denial constraint transformation
• 4.2 Optimized solution
• 4.3 Scalable strategies
• Chapter 4 Pattern discovery for heterogeneous data
• 4.1 Problem definition for multi-source heterogeneous data
• 4.2 Optimization framework
• 4.3 PatternFinder algorithm
• 4.4 The optimized grouping strategy
• Chapter 5 Deep fact discovery for text data
• 5.1 Fact extraction via mining patterns
• 5.2 The CNN-LSTM architecture
• 5.3 The fact encoder and pattern embedding
• 5.4 Training and inference.
• (source: Nielsen Book Data)

This book addresses several knowledge discovery problems on multi-sourced data where the theories, techniques, and methods in data cleaning, data mining, and natural language processing are synthetically used. This book mainly focuses on three data models: the multi-sourced isomorphic data, the multi-sourced heterogeneous data, and the text data. On the basis of three data models, this book studies the knowledge discovery problems including truth discovery and fact discovery on multi-sourced data from four important properties: relevance, inconsistency, sparseness, and heterogeneity, which is useful for specialists as well as graduate students. Data, even describing the same object or event, can come from a variety of sources such as crowd workers and social media users. However, noisy pieces of data or information are unavoidable. Facing the daunting scale of data, it is unrealistic to expect humans to "label" or tell which data source is more reliable. Hence, it is crucial to identify trustworthy information from multiple noisy information sources, referring to the task of knowledge discovery. At present, the knowledge discovery research for multi-sourced data mainly faces two challenges. On the structural level, it is essential to consider the different characteristics of data composition and application scenarios and define the knowledge discovery problem on different occasions. On the algorithm level, the knowledge discovery task needs to consider different levels of information conflicts and design efficient algorithms to mine more valuable information using multiple clues. Existing knowledge discovery methods have defects on both the structural level and the algorithm level, making the knowledge discovery problem far from totally solved.
(source: Nielsen Book Data)

• 1. Introduction to Data Science and Machine Learning Algorithms.-
• 2. Identifying Indicators of Global Financial Crisis with Fuzzy Logic and Data Science: A Comparative Analysis between Developing and Developed Economies.-
• 3. Determining the Ways to Increase Economic Growth of Developing and Developed Economies: An Application with Data Mining and Fuzzy TOPSIS.-
• 4. Profitability Prediction of Turkish Banking Industry: A Comparative Analysis with Data Science and Fuzzy ANP.-
• 5. The Influence of the Politicians on Macroeconomic Performance: An Analysis of Donald Trump's Tweets.-
• 6. How is the Stock Exchange Index Affected by the Disclosures of Politicians?.-
• 7. Defining the Significant Factors of Currency Exchange Rate Risk by Considering Text Mining and Fuzzy AHP.-
• 8. Emerging Applications and the Future of Data Science.
• (source: Nielsen Book Data)

This book considers and assesses essential financial issues by utilizing data science and fuzzy multiple criteria decision making (MCDM) methods. It introduces readers to a range of data science methods, and demonstrates their application in the fields of business, health, economics, finance and engineering. In addition, it provides suggestions based on the assessment results on each topic, which can help to enhance the efficiency of the financial system and the sustainability of economic development. Given its scope, the book will help readers broaden their perspective on the assessment and evaluation of financial issues using data science and MCDM approaches.
(source: Nielsen Book Data)

• Introduction
• Chapter 1: The Business Side of Data Science
• Chapter 2: Working with Modern Data
• Chapter 3: Telling the Story, Summarizing the Data
• Chapter 4: Machine Learning
• Chapter 5: Knowing the Tools
• Chapter 6: Deep Learning and Artificial Intelligence Postscript Index.
• (source: Nielsen Book Data)

Tap into the power of data science with this comprehensive resource for non-technical professionals Data Science: The Executive Summary - A Technical Book for Non-Technical Professionals is a comprehensive resource for people in non-engineer roles who want to fully understand data science and analytics concepts. Accomplished data scientist and author Field Cady describes both the "business side" of data science, including what problems it solves and how it fits into an organization, and the technical side, including analytical techniques and key technologies. Data Science: The Executive Summary covers topics like: Assessing whether your organization needs data scientists, and what to look for when hiring them When Big Data is the best approach to use for a project, and when it actually ties analysts' hands Cutting edge Artificial Intelligence, as well as classical approaches that work better for many problems How many techniques rely on dubious mathematical idealizations, and when you can work around them Perfect for executives who make critical decisions based on data science and analytics, as well as mangers who hire and assess the work of data scientists, Data Science: The Executive Summary also belongs on the bookshelves of salespeople and marketers who need to explain what a data analytics product does. Finally, data scientists themselves will improve their technical work with insights into the goals and constraints of the business situation. .
(source: Nielsen Book Data)

Human-in-the-Loop Machine Learning lays out methods for humans and machines to work together effectively. You'll find best practices on selecting sample data for human feedback, quality control for human annotations, and designing annotation interfaces. You'll learn to create training data for labeling, object detection, and semantic segmentation, sequence labeling, and more. The book starts with the basics and progresses to advanced techniques like transfer learning and self-supervision within annotation workflows.

• 1: Survival of the Sweaty Patter-Processors
• 2: Predicting What is Predictable
• 3: Duped and Deceived
• 4: Fooled Again and Again
• 5: The Paradox of Big Data
• 6: Fruitless Searches
• 7: The Reproducibility Crisis
• 8: Who Stepped In It?
• 9: Seeing Things for What They Are.
• (source: Nielsen Book Data)

Pattern-recognition prowess served our ancestors well, but today we are confronted by a deluge of data that is far more abstract, complicated, and difficult to interpret. The number of possible patterns that can be identified relative to the number that are genuinely useful has grown exponentially - which means that the chances that a discovered pattern is useful is rapidly approaching zero. Patterns in data are often used as evidence, but how can you tell if that evidence is worth believing? We are hard-wired to notice patterns and to think that the patterns we notice are meaningful. Streaks, clusters, and correlations are the norm, not the exception. Our challenge is to overcome our inherited inclination to think that all patterns are significant, as in this age of Big Data patterns are inevitable and usually coincidental. Through countless examples, The Phantom Pattern Problem is an engaging read that helps us avoid being duped by data, tricked into worthless investing strategies, or scared out of getting vaccinations.
(source: Nielsen Book Data)

• List of Symbols.- List of Figures.- List of Tables.- Introduction.- The Traditional Approach: Gross Scoring.- Basic Net Scoring Methods: The Uplift Approach.- Validation of Net Models: Measuring Stability and Discriminatory Power.- Supplementary Methods for Variable Transformation and Selection.- A Simulation Framework for the Validation of Research Hypotheses on Net Scoring.- Software Implementations.- Data Prerequisites.- Practical Issues and Business Cases.- Summary and Outlook.- Appendix.- Other Literature on Net Scoring.- Index.-.
• (source: Nielsen Book Data)

• 4.2.3 Model Stability Rank Correlation4.3 Discriminatory Power; 4.3.1 Qini; 4.3.2 AUnROC; 4.3.3 A Significance-Based Measure; 4.4 Model Validation and Adjustment Over Time; References; 5 Supplementary Methods for Variable Transformation and Selection; 5.1 Variable Transformation; 5.1.1 Binning Methods; 5.1.2 Transformation of Categorical to Numerical Variables; 5.2 Variable Preselection; 5.2.1 Summary of Preselection Methods; 5.2.1.1 Net Scoring with Just One Layer of a Decision Tree Procedure; 5.2.1.2 χ2net, 2 Statistic with More Than Two Values; 5.2.1.3 χ2 Statistic with Uplift Impact

This book explores all relevant aspects of net scoring, also known as uplift modeling: a data mining approach used to analyze and predict the effects of a given treatment on a desired target variable for an individual observation. After discussing modern net score modeling methods, data preparation, and the assessment of uplift models, the book investigates software implementations and real-world scenarios. Focusing on the application of theoretical results and on practical issues of uplift modeling, it also includes a dedicated chapter on software solutions in SAS, R, Spectrum Miner, and KNIME, which compares the respective tools. This book also presents the applications of net scoring in various contexts, e.g. medical treatment, with a special emphasis on direct marketing and corresponding business cases. The target audience primarily includes data scientists, especially researchers and practitioners in predictive modeling and scoring, mainly, but not exclusively, in the marketing context.
(source: Nielsen Book Data)

• Privacy-Preserving Top-k Query Processing in Distributed Systems.- Trust Factors and Insider Threats in Permissioned Distributed Ledgers - An Analytical Study and Evaluation of Popular DLT Frameworks.- Polystore and Tensor Data Model for Logical Data Independence and Impedance Mismatch in Big Data Analytics.- A General Framework for Multiple Choice Question Answering Based on Mutual Information and Reinforced Co-occurrence.- Rejig: A Scalable Online Algorithm for Cache Server Configuration Changes.
• (source: Nielsen Book Data)

The LNCS journal Transactions on Large-Scale Data- and Knowledge-Centered Systems focuses on data management, knowledge discovery, and knowledge processing, which are core and hot topics in computer science. Since the 1990s, the Internet has become the main driving force behind application development in all domains. An increase in the demand for resource sharing across different sites connected through networks has led to an evolution of data- and knowledge-management systems from centralized systems to decentralized systems enabling large-scale distributed applications providing high scalability. Current decentralized systems still focus on data and knowledge as their main resource. Feasibility of these systems relies basically on P2P (peer-to-peer) techniques and the support of agent systems with scaling and decentralized control. Synergy between grids, P2P systems, and agent technologies is the key to data- and knowledge-centered systems in large-scale environments. This, the 42nd issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, consists of five revised selected regular papers, presenting the following topics: Privacy-Preserving Top-k Query Processing in Distributed Systems; Trust Factors and Insider Threats in Permissioned Distributed Ledgers: An Analytical Study and Evaluation of Popular DLT Frameworks; Polystore and Tensor Data Model for Logical Data Independence and Impedance Mismatch in Big Data Analytics; A General Framework for Multiple Choice Question Answering Based on Mutual Information and Reinforced Co-occurrence; Rejig: A Scalable Online Algorithm for Cache Server Configuration Changes. .
(source: Nielsen Book Data)

• Table of Contents The Splunk 7 Interface Understanding Search Tables, Charts and Fields Data Models and Pivots Simple XML Dashboards Advanced Search Examples Extending Search Working with Apps Building Advanced Dashboards Summary Indexes and CSV Files Configuring Splunk Advanced Deployments Extending Splunk Machine Learning Toolkit.
• (source: Nielsen Book Data)

A comprehensive guide to making machine data accessible across the organization using advanced dashboards Key Features Enrich machine-generated data and transform it into useful, meaningful insights Perform search operations and configurations, build dashboards, and manage logs Extend Splunk services with scripts and advanced configurations to process optimal results Book DescriptionSplunk is the leading platform that fosters an efficient methodology and delivers ways to search, monitor, and analyze growing amounts of big data. This book will allow you to implement new services and utilize them to quickly and efficiently process machine-generated big data. We introduce you to all the new features, improvements, and offerings of Splunk 7. We cover the new modules of Splunk: Splunk Cloud and the Machine Learning Toolkit to ease data usage. Furthermore, you will learn to use search terms effectively with Boolean and grouping operators. You will learn not only how to modify your search to make your searches fast but also how to use wildcards efficiently. Later you will learn how to use stats to aggregate values, a chart to turn data, and a time chart to show values over time; you'll also work with fields and chart enhancements and learn how to create a data model with faster data model acceleration. Once this is done, you will learn about XML Dashboards, working with apps, building advanced dashboards, configuring and extending Splunk, advanced deployments, and more. Finally, we teach you how to use the Machine Learning Toolkit and best practices and tips to help you implement Splunk services effectively and efficiently. By the end of this book, you will have learned about the Splunk software as a whole and implemented Splunk services in your tasks at projects What you will learn Focus on the new features of the latest version of Splunk Enterprise 7 Master the new offerings in Splunk: Splunk Cloud and the Machine Learning Toolkit Create efficient and effective searches within the organization Master the use of Splunk tables, charts, and graph enhancements Use Splunk data models and pivots with faster data model acceleration Master all aspects of Splunk XML dashboards with hands-on applications Create and deploy advanced Splunk dashboards to share valuable business insights with peers Who this book is forThis book is intended for data analysts, business analysts, and IT administrators who want to make the best use of big data, operational intelligence, log management, and monitoring within their organization. Some knowledge of Splunk services will help you get the most out of the book.
(source: Nielsen Book Data)

Online social networking sites like Facebook, LinkedIn, and Twitter, offer millions of members the opportunity to befriend one another, send messages to each other, and post content on the site - actions which generate mind-boggling amounts of data every day.To make sense of the massive data from these sites, we resort to social media mining to answer questions like the following:Social media shatters the boundaries between the real world and the virtual world. We can now integrate social theories with computational methods to study how individuals interact with each other and how social communities form in bipartite and signed networks. The uniqueness of social media data calls for novel data mining techniques that can effectively handle user generated content with rich social relations. The study and development of these new techniques are under the purview of social media mining, an emerging discipline under the umbrella of data mining. Social Media Mining is the process of representing, analyzing, and extracting actionable patterns from social media data.
(source: Nielsen Book Data)

• Data Mining Fundamentals
• Data Mining: A First View DATA SCIENCE, ANALYTICS, MINING, AND KNOWLEDGE DISCOVERY IN DATABASES WHAT CAN COMPUTERS LEARN? IS DATA MINING APPROPRIATE FOR MY PROBLEM? DATA MINING OR KNOWLEDGE ENGINEERING? A NEAREST NEIGHBOR APPROACH DATA MINING, BIG DATA, AND CLOUD COMPUTING DATA MINING ETHICS INTRINSIC VALUE AND CUSTOMER CHURN CHAPTER SUMMARY KEY TERMS
• Data Mining: A Closer Look DATA MINING STRATEGIES SUPERVISED DATA MINING TECHNIQUES ASSOCIATION RULES CLUSTERING TECHNIQUES EVALUATING PERFORMANCE CHAPTER SUMMARY KEY TERMS
• Basic Data Mining Techniques CHAPTER OBJECTIVES DECISION TREES A BASIC COVERING RULE ALGORITHM GENERATING ASSOCIATION RULES THE K-MEANS ALGORITHM GENETIC LEARNING CHOOSING A DATA MINING TECHNIQUE CHAPTER SUMMARY KEY TERMS
• Tools for Knowledge Discovery
• Weka-An Environment for Knowledge Discovery GETTING STARTED WITH WEKA BUILDING DECISION TREES GENERATING PRODUCTION RULES WITH PART ATTRIBUTE SELECTION AND NEAREST NEIGHBOR CLASSIFICATION ASSOCIATION RULES COST/BENEFIT ANALYSIS UNSUPERVISED CLUSTERING WITH THE K-MEANS ALGORITHM CHAPTER SUMMARY
• Knowledge Discovery with RapidMiner GETTING STARTED WITH RAPIDMINER BUILDING DECISION TREES GENERATING RULES ASSOCIATION RULE LEARNING UNSUPERVISED CLUSTERING WITH K-MEANS ATTRIBUTE SELECTION AND NEAREST NEIGHBOR CLASSIFICATION CHAPTER SUMMARY
• The Knowledge Discovery Process A PROCESS MODEL FOR KNOWLEDGE DISCOVERY GOAL IDENTIFICATION 2016.3 CREATING A TARGET DATA SET DATA PREPROCESSING DATA TRANSFORMATION DATA MINING INTERPRETATION AND EVALUATION TAKING ACTION THE CRISP-DM PROCESS MODEL CHAPTER SUMMARY KEY TERMS
• Formal Evaluation Techniques WHAT SHOULD BE EVALUATED? TOOLS FOR EVALUATION COMPUTING TEST SET CONFIDENCE INTERVALS COMPARING SUPERVISED LEARNER MODELS UNSUPERVISED EVALUATION TECHNIQUES EVALUATING SUPERVISED MODELS WITH NUMERIC OUTPUT COMPARING MODELS WITH RAPIDMINER ATTRIBUTE EVALUATION FOR MIXED DATA TYPES PARETO LIFT CHARTS CHAPTER SUMMARY KEY TERMS
• Building Neural Networks
• Neural Networks FEED-FORWARD NEURAL NETWORKS NEURAL NETWORK TRAINING: A CONCEPTUAL VIEW NEURAL NETWORK EXPLANATION GENERAL CONSIDERATIONS NEURAL NETWORK TRAINING: A DETAILED VIEW CHAPTER SUMMARY KEY TERMS
• Building Neural Networks with Weka DATA SETS FOR BACKPROPAGATION LEARNING MODELING THE EXCLUSIVE-OR FUNCTION: NUMERIC OUTPUT MODELING THE EXCLUSIVE-OR FUNCTION: CATEGORICAL OUTPUT MINING SATELLITE IMAGE DATA UNSUPERVISED NEURAL NET CLUSTERING CHAPTER SUMMARY KEY TERMS
• Building Neural Networks with RapidMiner MODELING THE EXCLUSIVE-OR FUNCTION MINING SATELLITE IMAGE DATA PREDICTING CUSTOMER CHURN RAPIDMINER'S SELF-ORGANIZING MAP OPERATOR CHAPTER SUMMARY
• Advanced Data Mining Techniques
• Supervised Statistical Techniques BAYES CLASSIFIER SUPPORT VECTOR MACHINES LINEAR REGRESSION ANALYSIS REGRESSION TREES LOGISTIC REGRESSION CHAPTER SUMMARY KEY TERMS
• Unsupervised Clustering Techniques AGGLOMERATIVE CLUSTERING CONCEPTUAL CLUSTERING EXPECTATION MAXIMIZATION GENETIC ALGORITHMS AND UNSUPERVISED CLUSTERING CHAPTER SUMMARY KEY TERMS
• Specialized Techniques TIME-SERIES ANALYSIS MINING THE WEB MINING TEXTUAL DATA TECHNIQUES FOR LARGE-SIZED, IMBALANCED, AND STREAMING DATA ENSEMBLE TECHNIQUES FOR IMPROVING PERFORMANCE CHAPTER SUMMARY KEY TERMS
• The Data Warehouse OPERATIONAL DATABASES DATA WAREHOUSE DESIGN ONLINE ANALYTICAL PROCESSING EXCEL PIVOT TABLES FOR DATA ANALYTICS CHAPTER SUMMARY KEY TERMS.
• (source: Nielsen Book Data)

Data Mining: A Tutorial-Based Primer, Second Edition provides a comprehensive introduction to data mining with a focus on model building and testing, as well as on interpreting and validating results. The text guides students to understand how data mining can be employed to solve real problems and recognize whether a data mining solution is a feasible alternative for a specific problem. Fundamental data mining strategies, techniques, and evaluation methods are presented and implemented with the help of two well-known software tools. Several new topics have been added to the second edition including an introduction to Big Data and data analytics, ROC curves, Pareto lift charts, methods for handling large-sized, streaming and imbalanced data, support vector machines, and extended coverage of textual data mining. The second edition contains tutorials for attribute selection, dealing with imbalanced data, outlier analysis, time series analysis, mining textual data, and more. The text provides in-depth coverage of RapidMiner Studio and Weka's Explorer interface. Both software tools are used for stepping students through the tutorials depicting the knowledge discovery process. This allows the reader maximum flexibility for their hands-on data mining experience. .
(source: Nielsen Book Data)

• 1. Event generation and system monitoring
• 2. Pattern discovery and summarization
• 3. Applications

• Introduction Tao Li Data-Driven System Management Overview of the Book Content of the Book Conclusion
• Event Generation and System Monitoring Event Generation: From Logs to Events Liang Tang and Tao Li Chapter Overview Log Parser Log Message Classification Log Message Clustering Tree Structure-Based Clustering Message Signature-Based Event Generation Summary
• Optimizing System Monitoring Configurations Liang Tang and Tao Li Chapter Overview Automatic Monitoring Eliminating False Positive Eliminating False Negative Evaluation Summary
• Pattern Discovery and Summarization Event Pattern Mining Chunqiu Zeng and Tao Li Introduction Sequential Pattern Fully Dependent Pattern Partially Periodic Dependent Pattern Mutually Dependent Pattern T-Pattern Frequent Episode Event Burst Rare Event Correlated Pattern between Time Series and Event A Case Study Conclusion
• Mining Time Lags Chunqiu Zeng, Liang Tang, and Tao Li Introduction Nonparametric Method Parametric Method Empirical Studies Summary
• Log Event Summarization Yexi Jiang and Tao Li Introduction Summarizing with Frequency Changing Summarizing with Temporal Dynamics Facilitating the Summarization Tasks Summary
• Applications Data-Driven Applications in System Management Wubai Zhou, Chunqiu Zeng, Liang Tang, and Tao Li System Diagnosis Searching Similar Sequential Textual Event Segments Hierarchical Multi-Label Ticket Classification Tickets Resolution Recommendation Summary
• Social Media Event Summarization Using Twitter Streams Chao Shen and Tao Li Introduction Problem Formulation Tweet Context Analysis Sub-Event Detection Methods Multi-Tweet Summarization Experiments Conclusion and Future Work
• A Glossary appears at the end of each chapter.
• (source: Nielsen Book Data)

• Introduction. Event Generation and System Monitoring. Pattern Discovery and Summarization. Applications.
• (source: Nielsen Book Data)

Event mining encompasses techniques for automatically and efficiently extracting valuable knowledge from historical event/log data. The field, therefore, plays an important role in data-driven system management. Event Mining: Algorithms and Applications presents state-of-the-art event mining approaches and applications with a focus on computing system management. The book first explains how to transform log data in disparate formats and contents into a canonical form as well as how to optimize system monitoring. It then shows how to extract useful knowledge from data. It describes intelligent and efficient methods and algorithms to perform data-driven pattern discovery and problem determination for managing complex systems. The book also discusses data-driven approaches for the detailed diagnosis of a system issue and addresses the application of event summarization in Twitter messages (tweets). Understanding the interdisciplinary field of event mining can be challenging as it requires familiarity with several research areas and the relevant literature is scattered in diverse publications. This book makes it easier to explore the field by providing both a good starting point for readers not familiar with the topics and a comprehensive reference for those already working in this area.
(source: Nielsen Book Data)
Event mining encompasses techniques for automatically and efficiently extracting valuable knowledge from historical event/log data. The field, therefore, plays an important role in data-driven system management. Event Mining: Algorithms and Applications presents state-of-the-art event mining approaches and applications with a focus on computing sys.
(source: Nielsen Book Data)

• Introduction to Reinforcement Learning. Model-Free Policy Iteration. Policy Iteration with Value Function Approximation. Basis Design for Value Function Approximation. Sample Reuse in Policy Iteration. Active Learning in Policy Iteration. Robust Policy Iteration. Model-Free Policy Search. Direct Policy Search by Gradient Ascent. Direct Policy Search by Expectation-Maximization. Policy-Prior Search. Model-Based Reinforcement Learning. Transition Model Estimation. Dimensionality Reduction for Transition Model Estimation.
• (source: Nielsen Book Data)

Although the terms "data mining" and "knowledge discovery and data mining" (KDDM) are sometimes used interchangeably, data mining is actually just one step in the KDDM process. Data mining is the process of extracting useful information from data, while KDDM is the coordinated process of understanding the business and mining the data in order to identify previously unknown patterns. Knowledge Discovery Process and Methods to Enhance Organizational Performance explains the knowledge discovery and data mining (KDDM) process in a manner that makes it easy for readers to implement. Sharing the insights of international KDDM experts, it details powerful strategies, models, and techniques for managing the full cycle of knowledge discovery projects. The book supplies a process-centric view of how to implement successful data mining projects through the use of the KDDM process. It discusses the implications of data mining including security, privacy, ethical and legal considerations. Provides an introduction to KDDM, including the various models adopted in academia and industry Details critical success factors for KDDM projects as well as the impact of poor quality data or inaccessibility to data on KDDM projects Proposes the use of hybrid approaches that couple data mining with other analytic techniques (e.g., data envelopment analysis, cluster analysis, and neural networks) to derive greater value and utility Demonstrates the applicability of the KDDM process beyond analytics Shares experiences of implementing and applying various stages of the KDDM process in organizations The book includes case study examples of KDDM applications in business and government. After reading this book, you will understand the critical success factors required to develop robust data mining objectives that are in alignment with your organization's strategic business objectives.
(source: Nielsen Book Data)

• Introduction
• Kweku-Muata Osei-Bryson and Corlane Barclay Overview on Knowledge Discovery via Data Mining Process Models
• Sumana Sharma An Integrated Knowledge Discovery and Data Mining Process Model
• Sumana Sharma and Kweku-Muata Osei-Bryson A Method for Formulating the Business Objectives of Data Mining Projects
• Sumana Sharma and Kweku-Muata Osei-Bryson The Application of the Business Understanding Phase of the CRISP-DM Approach to a Knowledge Discovery Project on Education
• Corlane Barclay A Context-Aware Framework for Supporting the Evaluation of Data Mining Results
• Kweku-Muata Osei-Bryson Issues and Considerations in the Application of Data Mining in Business
• Edward Chen The Importance of Data Quality Assurance to the Data Analysis Activities of the Data Mining Process
• Patricia E. Nalwoga Lutu Critical Success Factors in Knowledge Discovery and Data Mining Projects
• Corlane Barclay Data Mining in Organizations: Opportunities and Challenges for Small Developing States
• Corlane Barclay Determining Sources of Relative Inefficiency in Heterogeneous Samples Using Multiple Data Analytic Techniques
• Sergey Samoilenko and Kweku-Muata Osei-Bryson Applications of Data Mining in Organizational Behavior
• Arash Shahin and Reza Salehzadeh Decision Making and Decision Styles of Project Managers: A Preliminary Exploration Using Data Mining Techniques
• Kweku-Muata Osei-Bryson and Corlane Barclay The Application of the CRISP-DM Process in Predicting High School Students' Examination (CSEC/CXC) Performance
• Corlane Barclay, Andrew Dennis, and Jerome Shepherd Post-Pruning in Decision Tree Induction Using Multiple Performance Measures
• Kweku-Muata Osei-Bryson Selecting Classifiers for an Ensemble-An Integrated Ensemble Generation Procedure
• Kweku-Muata Osei-Bryson A New Feature Selection Technique Applied to Credit Scoring Data Using a Rank Aggregation Approach Based on Optimization, Genetic Algorithm, and Similarity
• Bouaguel Waad, Bel Mufti Ghazi, and Limam Mohamed.
• (source: Nielsen Book Data)

• Introduction to Reinforcement Learning. Model-Free Policy Iteration. Policy Iteration with Value Function Approximation. Basis Design for Value Function Approximation. Sample Reuse in Policy Iteration. Active Learning in Policy Iteration. Robust Policy Iteration. Model-Free Policy Search. Direct Policy Search by Gradient Ascent. Direct Policy Search by Expectation-Maximization. Policy-Prior Search. Model-Based Reinforcement Learning. Transition Model Estimation. Dimensionality Reduction for Transition Model Estimation.
• (source: Nielsen Book Data)

Although the terms "data mining" and "knowledge discovery and data mining" (KDDM) are sometimes used interchangeably, data mining is actually just one step in the KDDM process. Data mining is the process of extracting useful information from data, while KDDM is the coordinated process of understanding the business and mining the data in order to identify previously unknown patterns. Knowledge Discovery Process and Methods to Enhance Organizational Performance explains the knowledge discovery and data mining (KDDM) process in a manner that makes it easy for readers to implement. Sharing the insights of international KDDM experts, it details powerful strategies, models, and techniques for managing the full cycle of knowledge discovery projects. The book supplies a process-centric view of how to implement successful data mining projects through the use of the KDDM process. It discusses the implications of data mining including security, privacy, ethical and legal considerations. Provides an introduction to KDDM, including the various models adopted in academia and industry Details critical success factors for KDDM projects as well as the impact of poor quality data or inaccessibility to data on KDDM projects Proposes the use of hybrid approaches that couple data mining with other analytic techniques (e.g., data envelopment analysis, cluster analysis, and neural networks) to derive greater value and utility Demonstrates the applicability of the KDDM process beyond analytics Shares experiences of implementing and applying various stages of the KDDM process in organizations The book includes case study examples of KDDM applications in business and government. After reading this book, you will understand the critical success factors required to develop robust data mining objectives that are in alignment with your organization's strategic business objectives.
(source: Nielsen Book Data)
Although the terms "data mining" and "knowledge discovery and data mining" (KDDM) are sometimes used interchangeably, data mining is actually just one step in the KDDM process. Data mining is the process of extracting useful information from data, while KDDM is the coordinated process of understanding the business and mining the data in order to id.
(source: Nielsen Book Data)

• 1. Introduction to data mining and RapidMiner
• 2. Basic classification use cases for credit approval and in education
• 3. Marketing, cross-selling, and recommender system use cases
• 4. Clustering in medical and educational domains
• 5. Text mining : spam detection, language detection, and customer feedback analysis
• 6. Feature selection and classification in astroparticle physics and in medical domains
• 7. Molecular structure- and property-activity relationship modeling in biochemistry and medicine
• 8. Image mining : feature extraction, segmentation, and classification
• 9. Anomaly detection, instance selection, and prototype construction
• 10. Meta-learning, automated learner selection, feature selection, and parameter optimization

RapidMiner is one of the most widely used open source data mining solutions world-wide. This book provides an application use case-based introduction to data mining and to RapidMiner (and RapidAnalytics.) It presents many different applications of data mining and how to implement them with RapidMiner, and it allows readers to get started with their own data mining applications with RapidMiner, or other similar tools. The software, the data sets, and RapidMiner data mining processes used and discussed in the book are made available to readers-- Provided by publisher