000			05875cam a2200493Ma 4500
001			ocn963702404
003			OCoLC
005			20190328114817.0
006			m o d
007			cr |n|||||||||
008			161118s2016 xx ob 001 0 eng d
040			_aIDEBK _beng _epn _cIDEBK _dN$T _dYDX _dEBLCP _dNLE _dOCLCO _dOPELS _dOCLCQ _dIDEBK _dOCLCF _dN$T _dUMI _dSTF _dIDB _dUPM _dOCLCQ _dOTZ _dOCLCQ _dU3W _dMERUC _dREB _dD6H _dCEF _dKSU _dDEBBG _dAU@ _dOCLCQ _dLVT _dCNCGM
019			_a964298618 _a964545062 _a967513911
020			_a0128118415 _q(electronic bk.)
020			_a9780128118412 _q(electronic bk.)
020			_z0128116544
020			_z9780128116548
035			_a(OCoLC)963702404 _z(OCoLC)964298618 _z(OCoLC)964545062 _z(OCoLC)967513911
050		4	_aQA76.9.D343
072		7	_aCOM _x000000 _2bisacsh
082	0	4	_a006.312 _223
100	1		_aYang, Yun.
245	1	0	_aTemporal Data Mining via Unsupervised Ensemble Learning / _h[electronic resource]
260			_bElsevier Science, _c2016.
300			_a1 online resource
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
588	0		_aPrint version record.
504			_aIncludes bibliographical references and index.
505	0		_aFront Cover -- Temporal Data Mining via Unsupervised Ensemble Learning -- Temporal Data Mining via Unsupervised Ensemble Learning -- Copyright -- Contents -- List of Figures -- List of Tables -- Acknowledgments -- 1 -- Introduction -- 1.1 BACKGROUND -- 1.2 PROBLEM STATEMENT -- 1.3 OBJECTIVE OF BOOK -- 1.4 OVERVIEW OF BOOK -- 2 -- Temporal Data Mining -- 2.1 INTRODUCTION -- 2.2 REPRESENTATIONS OF TEMPORAL DATA -- 2.2.1 TIME DOMAIN-BASED REPRESENTATIONS -- 2.2.2 TRANSFORMATION-BASED REPRESENTATIONS -- Piecewise Local Statistics -- Piecewise Discrete Wavelet Transforms -- Polynomial Curve Fitting -- Discrete Fourier Transforms -- 2.2.3 GENERATIVE MODEL-BASED REPRESENTATIONS -- 2.3 SIMILARITY MEASURES -- 2.3.1 SIMILARITY IN TIME -- 2.3.2 SIMILARITY IN SHAPE -- 2.3.3 SIMILARITY IN CHANGE -- 2.4 MINING TASKS -- 2.5 SUMMARY -- 3 -- Temporal Data Clustering -- 3.1 INTRODUCTION -- 3.2 OVERVIEW OF CLUSTERING ALGORITHMS -- 3.2.1 PARTITIONAL CLUSTERING -- K-means -- Hidden Markov Model-Based K-Models Clustering -- 3.2.2 HIERARCHICAL CLUSTERING -- Single Linkage -- Complete Linkage -- Average Linkage -- HMM-Based Agglomerative Clustering -- HMM-Based Divisive Clustering -- 3.2.3 DENSITY-BASED CLUSTERING -- Density-Based Spatial Clustering of Applications with Noise -- 3.2.4 MODEL-BASED CLUSTERING -- EM Algorithm -- HMM-Based Hybrid Partitional-Hierarchical Clustering -- HMM-Based Hierarchical Metaclustering -- 3.3 CLUSTERING VALIDATION -- 3.3.1 CLASSIFICATION ACCURACY -- 3.3.2 ADJUSTED RAND INDEX -- 3.3.3 JACCARD INDEX -- 3.3.4 MODIFIED HUBERT'S <U+0044> INDEX -- 3.3.5 DUNN'S VALIDITY INDEX -- 3.3.6 DAVIES-BOULDIN VALIDITY INDEX -- 3.3.7 NORMALIZED MUTUAL INFORMATION -- 3.4 SUMMARY -- 4 -- Ensemble Learning -- 4.1 INTRODUCTION -- 4.2 ENSEMBLE LEARNING ALGORITHMS -- Bagging -- Boosting -- 4.3 COMBINING METHODS -- Linear Combiner -- Product Combiner.
505	8		_aMajority Voting Combiner -- 4.4 DIVERSITY OF ENSEMBLE LEARNING -- 4.5 CLUSTERING ENSEMBLE -- 4.5.1 CONSENSUS FUNCTIONS -- 4.5.1.1 Hypergraphic Partitioning Approach -- Cluster-Based Similarity Partitioning Algorithm -- Hypergraph-Partitioning Algorithm -- Meta-Clustering Algorithm -- 4.5.1.2 Coassociation-Based Approach -- 4.5.1.3 Voting-Based Approach -- 4.5.2 OBJECTIVE FUNCTION -- 4.6 SUMMARY -- 5 -- HMM-Based Hybrid Meta-Clustering in Association With Ensemble Technique -- 5.1 INTRODUCTION -- 5.2 HMM-BASED HYBRID META-CLUSTERING ENSEMBLE -- 5.2.1 MOTIVATION -- 5.2.2 MODEL DESCRIPTION -- 5.3 SIMULATION -- 5.3.1 HMM-GENERATED DATA SET -- 5.3.2 CBF DATA SET -- 5.3.3 TIME SERIES BENCHMARKS -- 5.3.4 MOTION TRAJECTORY -- 5.4 SUMMARY -- 6 -- Unsupervised Learning via an Iteratively Constructed Clustering Ensemble -- 6.1 INTRODUCTION -- 6.2 ITERATIVELY CONSTRUCTED CLUSTERING ENSEMBLE -- 6.2.1 MOTIVATION -- 6.2.2 MODEL DESCRIPTION -- 6.3 SIMULATION -- 6.3.1 CYLINDER-BELL-FUNNEL DATA SET -- 6.3.2 TIME SERIES BENCHMARKS -- 6.3.3 MOTION TRAJECTORY -- 6.4 SUMMARY -- 7 -- Temporal Data Clustering via a Weighted Clustering Ensemble With Different Representations -- 7.1 INTRODUCTION -- 7.2 WEIGHTED CLUSTERING ENSEMBLE WITH DIFFERENT REPRESENTATIONS OF TEMPORAL DATA -- 7.2.1 MOTIVATION -- 7.2.2 MODEL DESCRIPTION -- 7.2.3 WEIGHTED CONSENSUS FUNCTION -- Partition Weighting Scheme -- Weighted Similarity Matrix -- Candidate Consensus Partition Generation -- 7.2.4 AGREEMENT FUNCTION -- 7.2.5 ALGORITHM ANALYSIS -- 7.3 SIMULATION -- 7.3.1 TIME SERIES BENCHMARKS -- 7.3.2 MOTION TRAJECTORY -- 7.3.3 TIME-SERIES DATA STREAM -- 7.4 SUMMARY -- 8 -- Conclusions, Future Work -- Appendix -- A.1 WEIGHTED CLUSTERING ENSEMBLE ALGORITHM ANALYSIS -- A.2 IMPLEMENTATION OF HMM-BASED META-CLUSTERING ENSEMBLE IN MATLAB CODE.
505	8		_aA.3 IMPLEMENTATION OF ITERATIVELY CONSTRUCTED CLUSTERING ENSEMBLE IN MATLAB CODE -- A.4 IMPLEMENTATION OF WCE WITH DIFFERENT REPRESENTATIONS -- References -- Index -- A -- B -- C -- D -- E -- F -- G -- H -- I -- J -- K -- L -- M -- N -- O -- P -- R -- S -- T -- V -- W -- Back Cover.
650		0	_aData mining.
650		0	_aTemporal databases.
650		0	_aMachine learning.
650		7	_aCOMPUTERS _xGeneral. _2bisacsh
650		7	_aData mining. _2fast _0(OCoLC)fst00887946
650		7	_aMachine learning. _2fast _0(OCoLC)fst01004795
650		7	_aTemporal databases. _2fast _0(OCoLC)fst01147471
655		4	_aElectronic books.
776	0	8	_iPrint version: _aYang, Yun. _tTemporal Data Mining via Unsupervised Ensemble Learning. _dElsevier Science, 2016 _z0128116544 _z9780128116548 _w(OCoLC)954534995
856	4	0	_3ScienceDirect _uhttp://www.sciencedirect.com/science/book/9780128116548
999			_c247469 _d247469