000			05826nam a2201273 i 4500
001			5265979
003			IEEE
005			20230927112347.0
006			m o d
007			cr |n|||||||||
008			100317t20152003njua ob 001 0 eng d
020			_a9780470544341 _qelectronic
020			_z9780470890455 _qprint
020			_z0470544341 _qelectronic
024	7		_a10.1109/9780470544341 _2doi
035			_a(CaBNVSL)mat05265979
035			_a(IDAMS)0b000064810c5b7f
040			_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL
082	0	4	_a006.3/12
100	1		_aKantardzic, Mehmed, _eauthor.
245	1	0	_aData mining : _bconcepts, models, methods, and algorithms / _cMehmed Kantardzic.
264		1	_aHoboken, New Jersey : _bWiley-Interscience, _c2003.
264		2	_a[Piscataqay, New Jersey] : _bIEEE Xplore, _c[2009]
300			_a1 PDF (xii, 345 pages) : _billustrations.
336			_atext _2rdacontent
337			_aelectronic _2isbdmedia
338			_aonline resource _2rdacarrier
504			_aIncludes bibliographical references and index.
505	0		_aPreparing the Data -- Data Reduction -- Learning from Data -- Statistical Methods -- Cluster Analysis -- Decision Trees and Decision Rules -- Association Rules -- Artificial Neural Networks -- Genetic Algorithms -- Fuzzy Sets and Fuzzy Logic -- Visualization Methods -- Data-Mining Tools -- Data-Mining Applications.
506	1		_aRestricted to subscribers or individual electronic text purchasers.
520			_aA comprehensive introduction to the exploding field of data mining We are surrounded by data, numerical and otherwise, which must be analyzed and processed to convert it into information that informs, instructs, answers, or otherwise aids understanding and decision-making. Due to the ever-increasing complexity and size of today's data sets, a new term, data mining, was created to describe the indirect, automatic data analysis techniques that utilize more complex and sophisticated tools than those which analysts used in the past to do mere data analysis. 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. This text offers guidance: how and when to use a particular software tool (with their 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. This 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. Data mining is an exploding field and this book offers much-needed guidance to selecting among the numerous analysis programs that are available.
530			_aAlso available in print.
538			_aMode of access: World Wide Web
588			_aDescription based on PDF viewed 12/21/2015.
650		0	_aData mining.
655		0	_aElectronic books.
695			_aNeurons
695			_aOptical fiber cables
695			_aOptimization
695			_aParameter estimation
695			_aPartitioning algorithms
695			_aPredictive models
695			_aRain
695			_aSections
695			_aServers
695			_aShape
695			_aStatistical analysis
695			_aSystem identification
695			_aTelecommunications
695			_aTemperature measurement
695			_aThree dimensional displays
695			_aTraining
695			_aTraining data
695			_aVisualization
695			_aAccuracy
695			_aAdaptive systems
695			_aAlgorithm design and analysis
695			_aAnalytical models
695			_aApproximation algorithms
695			_aApproximation methods
695			_aArtificial neural networks
695			_aAssociation rules
695			_aBibliographies
695			_aBiographies
695			_aBiological cells
695			_aBusiness
695			_aCable TV
695			_aCities and towns
695			_aClassification algorithms
695			_aClassification tree analysis
695			_aClustering algorithms
695			_aCommunities
695			_aCompanies
695			_aComplexity theory
695			_aComputational modeling
695			_aData analysis
695			_aData mining
695			_aData models
695			_aData visualization
695			_aDatabases
695			_aDispersion
695			_aDistributed databases
695			_aEncoding
695			_aEstimation
695			_aFault tolerance
695			_aFuzzy logic
695			_aFuzzy sets
695			_aGallium
695			_aGenerators
695			_aGenetic algorithms
695			_aGenetics
695			_aHumans
695			_aHypercubes
695			_aImage color analysis
695			_aIndexes
695			_aIndustries
695			_aItemsets
695			_aLearning
695			_aLearning systems
695			_aLoss measurement
695			_aMachine learning
695			_aMathematical model
695			_aMeasurement uncertainty
710	2		_aJohn Wiley & Sons, _epublisher.
710	2		_aIEEE Xplore (Online service), _edistributor.
776	0	8	_iPrint version: _z9780470890455
856	4	2	_3Abstract with links to resource _uhttps://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=5265979
999			_c40208 _d40208