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• Preface to the second edition
• Preface to the first edition
• 1. Introduction
• 2. Document file preparation
• 3. Vector space models
• 4. Matrix decompositions
• 5. Query management
• 6. Ranking and relevance feedback
• 7. Searching by link structure
• 8. User interface considerations
• 9. Further reading
• Bibliography
• Index.
• (source: Nielsen Book Data)

The second edition of Understanding Search Engines: Mathematical Modeling and Text Retrieval follows the basic premise of the first edition by discussing many of the key design issues for building search engines and emphasizing the important role that applied mathematics can play in improving information retrieval. The authors discuss important data structures, algorithms, and software as well as user-centered issues such as interfaces, manual indexing, and document preparation. Significant changes bring the text up to date on current information retrieval methods: for example the addition of a new chapter on link-structure algorithms used in search engines such as Google. The chapter on user interface has been rewritten to specifically focus on search engine usability. In addition the authors have added new recommendations for further reading and expanded the bibliography, and have updated and streamlined the index to make it more reader friendly.
(source: Nielsen Book Data)

• Introduction
• Document file preparation
• Vector space models
• Matrix decompositions
• Query management
• Ranking and relevance feedback
• User interface considerations
• A course project
• Further reading.

A discussion of many of the key design issues for building search engines. It emphasizes the important roles that applied mathematics can play in improving information retrieval. The authors discuss not only important data structures, algorithms and software, but also user-centred issues such as interfaces, manual indexing, and document preparation. The authors bridge the gap between applied mathematics and information retrieval. They discuss some of the current problems in information retrieval that may not be familiar to applied mathematicians and computer scientists and present some of the driving computational methods (SVD, SDD) for automated conceptual indexing. This book introduces topics in a non-technical way and provides insights into common problems found in information retrieval. The more mathematical details are provided in sidebars or offset from the regular text.
(source: Nielsen Book Data)

• Parallel Numerical Computing from Illiac IV to Exascale--The Contributions of Ahmed H. Sameh / Kyle A. Gallivan, Efstratios Gallopoulos
• Computational Capacity-Based Codesign of Computer Systems / David J. Kuck
• Measuring Computer Performance / William Jalby, David C. Wong, David J. Kuck
• A Compilation Framework for the Automatic Restructuring of Pointer-Linked Data Structures / Harmen L.A. van der Spek, C.W. Mattias Holm
• Dense Linear Algebra on Accelerated Multicore Hardware / Jack Dongarra, Jakub Kurzak, Piotr Luszczek
• The Explicit Spike Algorithm: Iterative Solution of the Reduced System / Carl Christian Kjelgaard Mikkelsen
• The Spike Factorization as Domain Decomposition Method; Equivalent and Variant Approaches / Victor Eijkhout, Robert van de Geijn
• Parallel Solution of Sparse Linear Systems / Murat Manguoglu
• Parallel Block-Jacobi SVD Methods / Martin Bečka, Gabriel Okša, Marián Vajteršic
• Robust and Efficient Multifrontal Solver for Large Discretized PDEs / Jianlin Xia
• A Preconditioned Scheme for Nonsymmetric Saddle-Point Problems / Abdelkader Baggag
• Effect of Ordering for Iterative Solvers in Structural Mechanics Problems / Sami A. Kilic
• Scaling Hypre's Multigrid Solvers to 100,000 Cores / Allison H. Baker, Robert D. Falgout
• A Riemannian Dennis-Moré Condition / Kyle A. Gallivan, Chunhong Qi, P.-A. Absil
• A Jump-Start of Non-negative Least Squares Solvers / Mu Wang, Xiaoge Wang
• Fast Nonnegative Tensor Factorization with an Active-Set-Like Method / Jingu Kim, Haesun Park
• Knowledge Discovery Using Nonnegative Tensor Factorization with Visual Analytics / Andrey A. Puretskiy, Michael W. Berry.

This book presents the state of the art in parallel numerical algorithms, applications, architectures, and system software. The book examines various solutions for issues of concurrency, scale, energy efficiency, and programmability, which are discussed in the context of a diverse range of applications. Features: includes contributions from an international selection of world-class authorities; examines parallel algorithm-architecture interaction through issues of computational capacity-based codesign and automatic restructuring of programs using compilation techniques; reviews emerging applications of numerical methods in information retrieval and data mining; discusses the latest issues in dense and sparse matrix computations for modern high-performance systems, multicores, manycores and GPUs, and several perspectives on the Spike family of algorithms for solving linear systems; presents outstanding challenges and developing technologies, and puts these in their historical context.
(source: Nielsen Book Data)

• Point Estimation Algorithms
• Applications of Bayes Theorem
• Similarity Measures
• Decision Trees
• Genetic Algorithms
• Classification: Distance Based Algorithms
• Decision Tree-Based Algorithms
• Covering (Rule-Based) Algorithms
• Clustering: An Overview
• Clustering Hierarchical Algorithms
• Clustering Partitional Algorithms
• Clustering: Large Databases
• Clustering Categorical Attributes
• Association Rules: An Overview
• Association Rules: Parallel and Distributed Algorithms
• Association Rules: Advanced Techniques and Measures
• Spatial Mining: Techniques and Algorithms.
• (source: Nielsen Book Data)

The continual explosion of information technology and the need for better data collection and management methods has made data mining an even more relevant topic of study. Books on data mining tend to be either broad and introductory or focus on some very specific technical aspect of the field.This book is a series of seventeen edited "student-authored lectures" which explore in depth the core of data mining (classification, clustering and association rules) by offering overviews that include both analysis and insight.The initial chapters lay a framework of data mining techniques by explaining some of the basics such as applications of Bayes Theorem, similarity measures, and decision trees. Before focusing on the pillars of classification, clustering and association rules, the book also considers alternative candidates such as point estimation and genetic algorithms.The book's discussion of classification includes an introduction to decision tree algorithms, rule-based algorithms (a popular alternative to decision trees) and distance-based algorithms. Five of the lecture-chapters are devoted to the concept of clustering or unsupervised classification. The functionality of hierarchical and partitional clustering algorithms is also covered as well as the efficient and scalable clustering algorithms used in large databases. The concept of association rules in terms of basic algorithms, parallel and distributive algorithms and advanced measures that help determine the value of association rules are discussed. The final chapter discusses algorithms for spatial data mining.
(source: Nielsen Book Data)

• Point Estimation Algorithms
• Applications of Bayes Theorem
• Similarity Measures
• Decision Trees
• Genetic Algorithms
• Classification: Distance Based Algorithms
• Decision Tree-Based Algorithms
• Covering (Rule-Based) Algorithms
• Clustering: An Overview
• Clustering Hierarchical Algorithms
• Clustering Partitional Algorithms
• Clustering: Large Databases
• Clustering Categorical Attributes
• Association Rules: An Overview
• Association Rules: Parallel and Distributed Algorithms
• Association Rules: Advanced Techniques and Measures
• Spatial Mining: Techniques and Algorithms.
• (source: Nielsen Book Data)

The continual explosion of information technology and the need for better data collection and management methods has made data mining an even more relevant topic of study. Books on data mining tend to be either broad and introductory or focus on some very specific technical aspect of the field.This book is a series of seventeen edited "student-authored lectures" which explore in depth the core of data mining (classification, clustering and association rules) by offering overviews that include both analysis and insight.The initial chapters lay a framework of data mining techniques by explaining some of the basics such as applications of Bayes Theorem, similarity measures, and decision trees. Before focusing on the pillars of classification, clustering and association rules, the book also considers alternative candidates such as point estimation and genetic algorithms.The book's discussion of classification includes an introduction to decision tree algorithms, rule-based algorithms (a popular alternative to decision trees) and distance-based algorithms. Five of the lecture-chapters are devoted to the concept of clustering or unsupervised classification. The functionality of hierarchical and partitional clustering algorithms is also covered as well as the efficient and scalable clustering algorithms used in large databases. The concept of association rules in terms of basic algorithms, parallel and distributive algorithms and advanced measures that help determine the value of association rules are discussed. The final chapter discusses algorithms for spatial data mining.
(source: Nielsen Book Data)

• Chapte
• r1: A Systematic Review on Supervised & Unsupervised Machine Learning Algorithms for Data Science.- Chapte
• r2: Overview of One-Pass and Discard-After-Learn Concepts for Classification and Clustering in Streaming Environment with Constraints.- Chapte
• r3: Distributed Single-Source Shortest Path Algorithms with Two Dimensional Graph Layout.- Chapte
• r4: Using Non-Negative Tensor Decomposition for Unsupervised Textual Influence Modeling.- Chapte
• r5: Survival Support Vector Machines: A Simulation Study and Its Health-related Application.- Chapte
• r6: Semantic Unsupervised Learning for Word Sense Disambiguation.- Chapte
• r7: Enhanced Tweet Hybrid Recommender System using Unsupervised Topic Modeling and Matrix Factorization based Neural Network.- Chapte
• r8: New Applications of a Supervised Computational Intelligence (CI) Approach: Case Study in Civil Engineering.
• (source: Nielsen Book Data)

This book covers the state of the art in learning algorithms with an inclusion of semi-supervised methods to provide a broad scope of clustering and classification solutions for big data applications. Case studies and best practices are included along with theoretical models of learning for a comprehensive reference to the field. The book is organized into eight chapters that cover the following topics: discretization, feature extraction and selection, classification, clustering, topic modeling, graph analysis and applications. Practitioners and graduate students can use the volume as an important reference for their current and future research and faculty will find the volume useful for assignments in presenting current approaches to unsupervised and semi-supervised learning in graduate-level seminar courses. The book is based on selected, expanded papers from the Fourth International Conference on Soft Computing in Data Science (2018). Includes new advances in clustering and classification using semi-supervised and unsupervised learning; Address new challenges arising in feature extraction and selection using semi-supervised and unsupervised learning; Features applications from healthcare, engineering, and text/social media mining that exploit techniques from semi-supervised and unsupervised learning.
(source: Nielsen Book Data)

• Artificial neural networks.- Classification, clustering, visualization.- Fuzzy logic.- Information and sentiment analytics.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the International Conference on Soft Computing in Data Science, SCDS 2016, held in Putrajaya, Malaysia, in September 2016. The 27 revised full papers presented were carefully reviewed and selected from 66 submissions. The papers are organized in topical sections on artificial neural networks; classification, clustering, visualization; fuzzy logic; information and sentiment analytics.
(source: Nielsen Book Data)

• Intro
• Preface
• Organization
• Contents
• Part I Data Mining
• An Improved Particle Swarm Optimization via Velocity-Based Reinitialization for Feature Selection
• 1 Introduction
• 2 Related Works
• 3 The Proposed PSO_ImVBR Method
• 4 Experimental Setup
• 5 Results and Discussion
• 6 Conclusion
• References
• Classifying Forum Questions Using PCA and Machine Learning for Improving Online CQA
• 1 Introduction
• 2 Experiment
• 2.1 CQA Dataset and Motivation
• 2.2 Classification Algorithms
• 2.3 Factor Analysis
• 2.4 Classification Model Performance
• 3 Conclusions

• 3.3 Missing Values
• 3.4 Typing Errors
• 3.5 Redundant Data
• 3.6 Incorrect Data Values
• 4 Findings and Discussion
• 4.1 Enhancement of Data Cleansing Process
• 4.2 Data Type Mismatch
• 4.3 Specification Mismatch
• 4.4 Missing Values
• 4.5 Typing Error
• 4.6 Redundant Data
• 4.7 Incorrect Data Values
• 5 Conclusion
• References
• Reviewing Classification Approaches in Sentiment Analysis
• 1 Introduction
• 2 Related Works
• 3 Sentiment Classification Approaches
• 3.1 Lexicon-Based Approach
• 3.2 Machine Learning Approach
• 4 Comparison of Sentiment Classification Approaches

• 5 Conclusion
• References
• Comparisons of ADABOOST, KNN, SVM and Logistic Regression in Classification of Imbalanced Dataset
• 1 Introduction
• 2 Literature Review
• 2.1 Machine Learning Techniques
• 2.2 Oversampling and Undersampling
• 3 Method
• 3.1 Data Set
• 3.2 Methods
• 4 Results and Discussions
• 5 Conclusions
• References
• Finding Significant Factors on World Ranking of e-Governments by Feature Selection Methods over KPIs
• 1 Introduction
• 2 Empirical Data Analysis
• 2.1 The Dataset
• 2.2 Feature Selection Algorithm
• 2.3 The Experimental Results

• 2.4 Comparison with Other Methods
• 3 Conclusions
• References
• Part II Fuzzy Computing
• Possibility Vague Soft Expert Set Theory and Its Application in Decision Making
• 1 Introduction
• 2 Preliminaries
• 3 Possibility Vague Soft Expert Sets
• 4 Basic Operations on Possibility Vague Soft Expert Sets
• 5 Application of PVSESs in a Decision Making Problem
• 6 Conclusion
• References
• An Iterative Method for Solving Fuzzy Fractional Differential Equations
• 1 Introduction
• 2 BasicConcepts
• 3 Solution Method
• 4 NumericalExperiment
• 5 Conclusion
• References

This book constitutes the refereed proceedings of the International Conference on Soft Computing in Data Science, SCDS 2015, held in Putrajaya, Malaysia, in September 2015. The 25 revised full papers presented were carefully reviewed and selected from 69 submissions. The papers are organized in topical sections on data mining; fuzzy computing; evolutionary computing and optimization; pattern recognition; human machine interface; hybrid methods.

Computational Information Retrieval Workshop held October 2000, Raleigh, North Carolina This volume contains selected papers that focus on the use of linear algebra, computational statistics, and computer science in the development of algorithms and software systems for text retrieval. Experts in information modeling and retrieval share their perspectives on the design of scalable but precise text retrieval systems, revealing many of the challenges and obstacles that mathematical and statistical models must overcome to be viable for automated text processing. This very useful proceedings is an excellent companion for courses in information retrieval, applied linear algebra, and applied statistics.
(source: Nielsen Book Data)

• Part I: Artificial Intelligence
• Extractive Text Summarization Using Syntactic Sub- Graph Models
• Analysis of Big Five Personality Factors to determine the Appropriate Type of Career using the C4.5 Algorithm
• Predicting Disaster Type from Social Media Imagery via Deep Neural Networks Directed by Visual Attention
• Dissemination Management for Official Statistics Using Artificial Intelligence-Based Media Monitoring
• Part II: Computational Vision
• A Naive but Effective Post-Processing Approach for Dark Channel Prior (DCP)
• COVID-19 Face Mask Classification Using Deep Learning
• Gender Classification Using Transfer Learning and Fine-Tuning
• Multi-Language Recognition Translator by Using the Convolutional Neural Network (CNN) Algorithm and Optical Character Recognition (OCR)
• Autonomous Driving Through Road Segmentation Based on Computer Vision Techniques
• Part III: Cybersecurity
• Phishing Attack Types and Mitigation: A Survey
• A Review of Privacy Protection Methods for Smart Homes Against Wireless Snooping Attack
• Development of Graph-Based Knowledge on Ransom-Ware Attacks Using Twitter Data
• Part IV: Big Data Analytics
• BigMDHealth: Supporting Multidimensional Big Data Management and Analytics over Big Healthcare Data via Effective and Efficient Multidimensional Aggregate Queries over Key-Value Stores (Prof Alredo's paper)
• Design and Implementation of Data Warehouse Solution at Kumpulan Wang Persaraan (KWAP)
• Consumer Behavior Prediction During Covid-19 Pandemic Conditions Using Sentiment Analytics
• Big Data Application on Prediction of HDD Manufacturing Process Performance
• Visualising Economic Situation Through Malaysia Economic Recovery Dashboard (MERD)
• Part V: Machine/Deep Learning
• Lung Nodules Classification Using Convolutional Neural Network with Transfer Learning
• Plant Growth Phase Classification Using Deep Neural Network
• The Implementation of Genetic Algorithm-Ensemble Learning on QSAR Study of Diacylglycerol Acyltransferase-1(DGAT1) Inhibitors as Anti-Diabetes
• Classification of Exercise Game Data for Rehabilitation Using Machine Learning Algorithms
• SDDLA: A New Architecture for Secured Decentralized Distributed Learning
• Gated Memory Unit: A Novel Recurrent Neural Network Architecture for Sequential Analysis
• Multi-Class Classification for Breast Cancer with High Dimensional Microarray Data Using Machine Learning Classifier
• Predicting Risks of Late Delivery to Online Shopping Customers Using Machine Learning Techniques
• Quora Insincere Questions Classification Using Attention Based Model
• Suicide Ideation Detection: A Comparative Study of Sequential and Transformer Hybrid Algorithms
• Well Log Data Preparation and Effective Utilization of Drilling Parameters Using Data Science Based Approaches
• Deep Learning-Based Approach for Classifying the Severity of Metal Corrosion Using SEM Images
• Insurance Risk Prediction Using Machine Learning
• Loan Default Forecasting Using StackNet
• Part VI: Statistical Learning
• Neural Network Autoregressive Model for Forecasting Malaysia Under-5 Mortality
• Robustness of Support Vector Regression and Random Forest Models: A Simulation Study
• The Impact of Restrictions Community Activities on COVID-19 Transmission: A Case Study in Sumatra Island, Indonesia
• Predicting Internet Usage for Digital Finance Services: Multitarget Classification Using Vector Generalized Additive Model with SMOTE-NC
• Part VII: Text Mining and Classification
• Identifying Topic Modeling Technique in Evaluating Textual Datasets
• Y-X-Y Encoding for Identifying Types of Sentence Similarity
• Evaluation of Extractive and Abstract Methods in Text Summarization.

The book presents selected papers from International Conference on Data Science and Emerging Technologies (DaSET 2022), held online at UNITAR International University, Malaysia, during December 2021, 2022. This book aims to present current research and applications of data science and emerging technologies. The deployment of data science and emerging technology contributes to the achievement of the Sustainable Development Goals for social inclusion, environmental sustainability, and economic prosperity. Data science and emerging technologies such as artificial intelligence and blockchain are useful for various domains such as marketing, health care, finance, banking, environmental, and agriculture. An important grand challenge in data science is to determine how developments in computational and social-behavioral sciences can be combined to improve well-being, emergency response, sustainability, and civic engagement in a well-informed, data-driven society. The topics of this book include, but not limited to: artificial intelligence, big data technology, machine and deep learning, data mining, optimization algorithms, blockchain, Internet of Things (IoT), cloud computing, computer vision, cybersecurity, augmented and virtual reality, cryptography, and statistical learning.

• AI Techniques and applications.- Data Analytics and Technologies.- Data Mining and Image Processing.- Machine & Statistical Learning. .
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 6th International Conference on Soft Computing in Data Science, SCDS 2021, which was held virtually in November 2021. The 31 revised full papers presented were carefully reviewed and selected from 79 submissions. The papers are organized in topical sections on AI techniques and applications; data analytics and technologies; data mining and image processing; machine & statistical learning. .
(source: Nielsen Book Data)

• List of Contributors. Preface. PART I TEXT EXTRACTION, CLASSIFICATION, AND CLUSTERING.
• 1 Automatic keyword extraction from individual documents. 1.1 Introduction. 1.2 Rapid automatic keyword extraction. 1.3 Benchmark evaluation. 1.4 Stoplist generation. 1.5 Evaluation on news articles. 1.6 Summary. 1.7 Acknowledgements.
• 2 Algebraic techniques for multilingual document clustering. 2.1 Introduction. 2.2 Background. 2.3 Experimental setup. 2.4 Multilingual LSA. 2.5 Tucker1 method. 2.6 PARAFAC2 method. 2.7 LSA with term alignments. 2.8 Latent morpho-semantic analysis (LMSA). 2.9 LMSA with term alignments. 2.10 Discussion of results and techniques. 2.11 Acknowledgements.
• 3 Content-based spam email classification using machine-learning algorithms. 3.1 Introduction. 3.2 Machine-learning algorithms. 3.3 Data preprocessing. 3.4 Evaluation of email classification. 3.5 Experiments. 3.6 Characteristics of classifiers. 3.7 Concluding remarks. 3.8 Acknowledgements.
• 4 Utilizing nonnegative matrix factorization for email classification problems. 4.1 Introduction. 4.2 Background. 4.3 NMF initialization based on feature ranking. 4.4 NMF-based classification methods. 4.5 Conclusions. 4.6 Acknowledgements.
• 5 Constrained clustering with k -means type algorithms. 5.1 Introduction. 5.2 Notations and classical k -means. 5.3 Constrained k -means with Bregman divergences. 5.4 Constrained smoka type clustering. 5.5 Constrained spherical k -means. 5.6 Numerical experiments. 5.7 Conclusion. PART II ANOMALY AND TREND DETECTION.
• 6 Survey of text visualization techniques. 6.1 Visualization in text analysis. 6.2 Tag clouds. 6.3 Authorship and change tracking. 6.4 Data exploration and the search for novel patterns. 6.5 Sentiment tracking. 6.6 Visual analytics and FutureLens. 6.7 Scenario discovery. 6.8 Earlier prototype. 6.9 Features of FutureLens. 6.10 Scenario discovery example: bioterrorism. 6.11 Scenario discovery example: drug trafficking. 6.12 Future work.
• 7 Adaptive threshold setting for novelty mining. 7.1 Introduction. 7.2 Adaptive threshold setting in novelty mining. 7.3 Experimental study. 7.4 Conclusion.
• 8 Text mining and cybercrime. 8.1 Introduction. 8.2 Current research in Internet predation and cyberbullying. 8.3 Commercial software for monitoring chat. 8.4 Conclusions and future directions. 8.5 Acknowledgements. PART III TEXT STREAMS.
• 9 Events and trends in text streams. 9.1 Introduction. 9.2 Text streams. 9.3 Feature extraction and data reduction. 9.4 Event detection. 9.5 Trend detection. 9.6 Event and trend descriptions. 9.7 Discussion. 9.8 Summary. 9.9 Acknowledgements.
• 10 Embedding semantics in LDA topic models. 10.1 Introduction. 10.2 Background. 10.3 Latent Dirichlet allocation. 10.4 Embedding external semantics from Wikipedia. 10.5 Data-driven semantic embedding. 10.6 Related work. 10.7 Conclusion and future work. References. Index.
• (source: Nielsen Book Data)

Text Mining: Applications and Theory presents the state-of-the-art algorithms for text mining from both the academic and industrial perspectives. The contributors span several countries and scientific domains: universities, industrial corporations, and government laboratories, and demonstrate the use of techniques from machine learning, knowledge discovery, natural language processing and information retrieval to design computational models for automated text analysis and mining. This volume demonstrates how advancements in the fields of applied mathematics, computer science, machine learning, and natural language processing can collectively capture, classify, and interpret words and their contexts. As suggested in the preface, text mining is needed when "words are not enough." This book: Provides state-of-the-art algorithms and techniques for critical tasks in text mining applications, such as clustering, classification, anomaly and trend detection, and stream analysis. Presents a survey of text visualization techniques and looks at the multilingual text classification problem. Discusses the issue of cybercrime associated with chatrooms. Features advances in visual analytics and machine learning along with illustrative examples. Is accompanied by a supporting website featuring datasets. Applied mathematicians, statisticians, practitioners and students in computer science, bioinformatics and engineering will find this book extremely useful.
(source: Nielsen Book Data)

Abstract not provided.

Abstract not provided.

• Cluster-preserving dimension reduction methods for document classification / Peg Howland, Haesun Park
• Automatic discovery of similar words / Pierre Senellart, Vincent D. Blondel
• Principal direction divisive partitioning with kernels and k-means steering / Dimitrios Zeimpekis, Efstratios Gallopoulos
• Hybrid clustering with divergences / Jacob Kogan, Charles Nicholas, Mike Wiacek
• Text clustering with local semantic kernels / Loulwah AlSumait, Carlotta Domeniconi
• Vector space models for search and cluster mining / Mei Kobayashi, Masaki Aono
• Applications of semidefinite programming in XML document classification / Zhonghang Xia ... [et al.]
• Discussion tracking in Enron email Using PARAFAC / Brett W. Bader, Michael W. Berry, Murray Browne
• Spam filtering based on latent semantic indexing / Wilfried N. Gansterer, Andreas G.K. Janecek, Robert Neumayer
• A probabilistic model for fast and confident categorization of textual documents / Cyril Goutte
• Anomaly detection using nonnegative matrix factorization / Edward G. Allan ... [et al.]
• Document representation and quality of text : an analysis / Mostafa Keikha ... [et al.]
• Appendix: SIAM text mining competition 2007.

The proliferation of digital computing devices and their use in communication has resulted in an increased demand for systems and algorithms capable of mining textual data. Thus, the development of techniques for mining unstructured, semi-structured, and fully-structured textual data has become increasingly important in both academia and industry. This second volume continues to survey the evolving field of text mining - the application of techniques of machine learning, in conjunction with natural language processing, information extraction and algebraic/mathematical approaches, to computational information retrieval. Numerous diverse issues are addressed, ranging from the development of new learning approaches to novel document clustering algorithms, collectively spanning several major topic areas in text mining. The book acts as an important benchmark in the development of current and future approaches to mining textual information; and serves as an excellent companion text for courses in text and data mining, information retrieval and computational statistics. Experts from academia and industry share their experiences in solving large-scale retrieval and classification problems. The book presents an overview of current methods and software for text mining; highlights open research questions in document categorization and clustering, and trend detection; describes new application problems in areas such as email surveillance and anomaly detection Survey of Text Mining II; and, offers a broad selection in state-of-the art algorithms and software for text mining from both academic and industrial perspectives, to generate interest and insight into the state of the field. This book will be an indispensable resource for researchers, practitioners, and professionals involved in information retrieval, computational statistics, and data mining. Michael W. Berry is a professor in the Department of Electrical Engineering and Computer Science at the University of Tennessee, Knoxville. Malu Castellanos is a senior researcher at Hewlett-Packard Laboratories in Palo Alto, California.
(source: Nielsen Book Data)