Data Mining: Concepts, Models, Methods, and Algorithms

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9780470890455: Data Mining: Concepts, Models, Methods, and Algorithms
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This book reviews state-of-the-art methodologies and techniques foranalyzing enormous quantities of raw data in high-dimensional dataspaces, to extract new information for decision making.The goal of this book is to provide a single introductorysource, organized in a systematic way, in which we could direct thereaders in analysis of large data sets, through the explanation ofbasic concepts, models and methodologies developed in recentdecades.

 

If you are an instructor or professor and would like to obtaininstructor’s materials, please visit http://booksupport.wiley.com

If you are an instructor or professor and would like to obtain asolutions manual, please send an email to: pressbooks@ieee.org

 

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From the Back Cover:

Now updated—the systematic introductory guide to modernanalysis of large data sets

As data sets continue to grow in size and complexity, there hasbeen an inevitable move towards indirect, automatic, andintelligent data analysis in which the analyst works via morecomplex and sophisticated software tools. This book reviewsstate-of-the-art methodologies and techniques for analyzingenormous quantities of raw data in high-dimensional data spaces toextract new information for decision-making.

This Second Edition of Data Mining: Concepts, Models,Methods, and Algorithms discusses data mining principles andthen describes representative state-of-the-art methods andalgorithms originating from different disciplines such asstatistics, machine learning, neural networks, fuzzy logic, andevolutionary computation. Detailed algorithms are provided withnecessary explanations and illustrative examples, and questions andexercises for practice at the end of each chapter. This new editionfeatures the following new techniques/methodologies:

  • Support Vector Machines (SVM)—developed based onstatistical learning theory, they have a large potential forapplications in predictive data mining

  • Kohonen Maps (Self-Organizing Maps - SOM)—one of veryapplicative neural-networks-based methodologies for descriptivedata mining and multi-dimensional data visualizations

  • DBSCAN, BIRCH, and distributed DBSCAN clusteringalgorithms—representatives of an important class ofdensity-based clustering methodologies

  • Bayesian Networks (BN) methodology often used for causalitymodeling

  • Algorithms for measuring Betweeness and Centrality parameters ingraphs, important for applications in mining large socialnetworks

  • CART algorithm and Gini index in building decision trees

  • Bagging & Boosting approaches to ensemble-learningmethodologies, with details of AdaBoost algorithm

  • Relief algorithm, one of the core feature selection algorithmsinspired by instance-based learning

  • PageRank algorithm for mining and authority ranking of webpages

  • Latent Semantic Analysis (LSA) for text mining and measuringsemantic similarities between text-based documents

  • New sections on temporal, spatial, web, text, parallel, anddistributed data mining

  • More emphasis on business, privacy, security, and legal aspectsof data mining technology

This text offers guidance on how and when to use a particularsoftware tool (with the companion data sets) from among thehundreds offered when faced with a data set to mine. This allowsanalysts to create and perform their own data mining experimentsusing their knowledge of the methodologies and techniques provided.The book emphasizes the selection of appropriate methodologies anddata analysis software, as well as parameter tuning. Thesecritically important, qualitative decisions can only be made withthe deeper understanding of parameter meaning and its role in thetechnique that is offered here.

This volume is primarily intended as a data-mining textbook forcomputer science, computer engineering, and computer informationsystems majors at the graduate level. Senior students at theundergraduate level and with the appropriate background can alsosuccessfully comprehend all topics presented here.

About the Author:

MEHMED KANTARDZIC, PhD, is a professor in the Department of Computer Engineering and Computer Science (CECS) in the Speed School of Engineering at the University of Louisville, Director of CECS Graduate Studies, as well as Director of the Data Mining Lab. A member of IEEE, ISCA, and SPIE, Dr. Kantardzic has won awards for several of his papers, has been published in numerous referred journals, and has been an invited presenter at various conferences. He has also been a contributor to numerous books.

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9780471228523: Data Mining: Concepts, Models, Methods, and Algorithms

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