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037			_a10.1002/9781118029145 _bWiley InterScience _nhttp://www3.interscience.wiley.com
050		4	_aQA76.9.D343 _bK36 2011
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082	0	4	_a006.3/12 _223
084			_a54.64 _2bcl
049			_aMAIN
100	1		_aKantardzic, Mehmed.
245	1	0	_aData mining : _bconcepts, models, methods, and algorithms / _cMehmed Kantardzic.
250			_aSecond edition.
264		1	_a[Piscataway, New Jersey] : _bIEEE Press ; _aHoboken, NJ : _bWiley, _c[2011]
264		4	_c©2011
300			_a1 online resource (xvii, 534 pages) : _billustrations
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
504			_aIncludes bibliographical references (pages 510-528) and index.
505	0	0	_tData-Mining Concepts -- _tPreparing the Data -- _tData Reduction -- _tLearning from Data -- _tStatistical Methods -- _tDecision Trees and Decision Rules -- _tArtificial Neural Networks -- _tEnsemble Learning -- _tCluster Analysis -- _tAssociation Rules -- _tWeb Mining and Text Mining -- _tAdvances in Data Mining -- _tGenetic Algorithms -- _tFuzzy sets and Fuzzy Logic -- _tVisualization Methods -- _tAppendix A -- _tAppendix B: Data-Mining Applications.
520			_aThis 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. The goal of this book is to provide 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.
520			_a"Now updated--the systematic introductory guide to modern analysis of large data sets. As data sets continue to grow in size and complexity, there has been an inevitable move towards indirect, automatic, and intelligent data analysis in which the analyst works via more complex and sophisticated software tools. 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. This Second Edition of Data Mining: Concepts, Models, Methods, and Algorithms discusses data mining principles and then describes representative state-of-the-art methods and algorithms originating from different disciplines such as statistics, machine learning, neural networks, fuzzy logic, and evolutionary computation. Detailed algorithms are provided with necessary explanations and illustrative examples, and questions and exercises for practice at the end of each chapter. This new edition features the following new techniques/methodologies: Support Vector Machines (SVM)--developed based on statistical learning theory, they have a large potential for applications in predictive data mining; Kohonen Maps (Self-Organizing Maps - SOM)--one of very applicative neural-networks-based methodologies for descriptive data mining and multi-dimensional data visualizations; DBSCAN, BIRCH, and distributed DBSCAN clustering algorithms--representatives of an important class of density-based clustering methodologies; Bayesian Networks (BN) methodology often used for causality modeling; Algorithms for measuring Betweeness and Centrality parameters in graphs, important for applications in mining large social networks; CART algorithm and Gini index in building decision trees; Bagging & Boosting approaches to ensemble-learning methodologies, with details of AdaBoost algorithm; Relief algorithm, one of the core feature selection algorithms inspired by instance-based learning; PageRank algorithm for mining and authority ranking of web pages; Latent Semantic Analysis (LSA) for text mining and measuring semantic similarities between text-based documents; New sections on temporal, spatial, web, text, parallel, and distributed data mining. More emphasis on business, privacy, security, and legal aspects of data mining technologyThis text offers guidance on how and when to use a particular software tool (with the companion data sets) from among the hundreds offered when faced with a data set to mine. This allows analysts to create and perform their own data mining experiments using their knowledge of the methodologies and techniques provided. The book emphasizes the selection of appropriate methodologies and data analysis software, as well as parameter tuning. These critically important, qualitative decisions can only be made with the deeper understanding of parameter meaning and its role in the technique that is offered here. This volume is primarily intended as a data-mining textbook for computer science, computer engineering, and computer information systems majors at the graduate level. Senior students at the undergraduate level and with the appropriate background can also successfully comprehend all topics presented here."--Publisher's description.
588	0		_aOnline resource and print version record; title from PDF title page (IEEE Xplore, viewed March 14, 2014).
650		0	_aData mining.
650		7	_aCOMPUTERS _xDatabase Management _xData Mining. _2bisacsh
650		7	_aData mining. _2fast _0(OCoLC)fst00887946
655		4	_aElectronic books.
776	0	8	_iPrint version: _aKantardzic, Mehmed. _tData mining. _b2nd ed. _dHoboken, N.J. : John Wiley : IEEE Press, ©2011 _z9780470890455 _w(DLC) 2011002190 _w(OCoLC)700735391
856	4	0	_uhttp://onlinelibrary.wiley.com/book/10.1002/9781118029145 _zWiley Online Library [Free Download only for SUST IP]
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