000			06825nam a2201297 i 4500
001			5236612
003			IEEE
005			20200421114110.0
006			m o d
007			cr |n|||||||||
008			090727t20152009njua ob 001 0 eng d
020			_a9780470382776 _qelectronic
020			_z0470276800 _qpaper
020			_z9780470276808 _qpaper
020			_z0470382783 _qelectronic
020			_z0470382775 _qelectronic
020			_z9780470382783 _qelectronic
024	7		_a10.1002/9780470382776 _2doi
035			_a(CaBNVSL)mat05236612
035			_a(IDAMS)0b00006481094c83
040			_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL
050		4	_aQA278 _b.X8 2009eb
050		4	_aQA278 _b.X87 2009eb
082	0		_a519.5/3 _222
082	0	4	_a519.53 _222
100	1		_aXu, Rui. _eauthor.
245	1	0	_aClustering / _cRui Xu, Donald C. Wunsch II.
264		1	_aPiscataway, New Jersey : _bIEEE Press, _cc2009.
264		2	_a[Piscataqay, New Jersey] : _bIEEE Xplore, _c2008.
300			_a1 PDF (x, 358 pages) : _billustrations.
336			_atext _2rdacontent
337			_aelectronic _2isbdmedia
338			_aonline resource _2rdacarrier
490	1		_aIEEE Press Series on Computational Intelligence ; _v10
504			_aIncludes bibliographical references and indexes.
505	0		_aPREFACE -- 1. CLUSTER ANALYSIS -- 1.1. Classifi cation and Clustering -- 1.2. Defi nition of Clusters -- 1.3. Clustering Applications -- 1.4. Literature of Clustering Algorithms -- 1.5. Outline of the Book -- 2. PROXIMITY MEASURES -- 2.1. Introduction -- 2.2. Feature Types and Measurement Levels -- 2.3. Defi nition of Proximity Measures -- 2.4. Proximity Measures for Continuous Variables -- 2.5. Proximity Measures for Discrete Variables -- 2.6. Proximity Measures for Mixed Variables -- 2.7. Summary -- 3. HIERARCHICAL CLUSTERING. -- 3.1. Introduction -- 3.2. Agglomerative Hierarchical Clustering -- 3.3. Divisive Hierarchical Clustering -- 3.4. Recent Advances -- 3.5. Applications -- 3.6. Summary -- 4. PARTITIONAL CLUSTERING -- 4.1. Introduction -- 4.2. Clustering Criteria -- 4.3. K-Means Algorithm -- 4.4. Mixture Density-Based Clustering -- 4.5. Graph Theory-Based Clustering -- 4.6. Fuzzy Clustering -- 4.7. Search Techniques-Based Clustering Algorithms -- 4.8. Applications -- 4.9. Summary -- 5. NEURAL NETWORK-BASED CLUSTERING -- 5.1. Introduction -- 5.2. Hard Competitive Learning Clustering -- 5.3. Soft Competitive Learning Clustering -- 5.4. Applications -- 5.5. Summary -- 6. KERNEL-BASED CLUSTERING -- 6.1. Introduction -- 6.2. Kernel Principal Component Analysis -- 6.3. Squared-Error-Based Clustering with Kernel Functions -- 6.4. Support Vector Clustering -- 6.5. Applications -- 6.6. Summary -- 7. SEQUENTIAL DATA CLUSTERING -- 7.1. Introduction -- 7.2. Sequence Similarity -- 7.3. Indirect Sequence Clustering -- 7.4. Model-Based Sequence Clustering -- 7.5. Applications-Genomic and Biological Sequence -- 7.6. Summary -- 8. LARGE-SCALE DATA CLUSTERING -- 8.1. Introduction -- 8.2. Random Sampling Methods -- 8.3. Condensation-Based Methods -- 8.4. Density-Based Methods -- 8.5. Grid-Based Methods -- 8.6. Divide and Conquer -- 8.7. Incremental Clustering -- 8.8. Applications -- 8.9. Summary -- 9. DATA VISUALIZATION AND HIGH-DIMENSIONAL DATA CLUSTERING.
505	8		_a9.1. Introduction -- 9.2. Linear Projection Algorithms -- 9.3. Nonlinear Projection Algorithms -- 9.4. Projected and Subspace Clustering -- 9.5. Applications -- 9.6. Summary -- 10. CLUSTER VALIDITY -- 10.1. Introduction -- 10.2. External Criteria -- 10.3. Internal Criteria -- 10.4. Relative Criteria -- 10.5. Summary -- 11. CONCLUDING REMARKS -- PROBLEMS -- REFERENCES -- AUTHOR INDEX -- SUBJECT INDEX.
506	1		_aRestricted to subscribers or individual electronic text purchasers.
516			_aText.
520			_aThis is the first book to take a truly comprehensive look at clustering. It begins with an introduction to cluster analysis and goes on to explore: proximity measures; hierarchical clustering; partition clustering; neural network-based clustering; kernel-based clustering; sequential data clustering; large-scale data clustering; data visualization and high-dimensional data clustering; and cluster validation. The authors assume no previous background in clustering and their generous inclusion of examples and references help make the subject matter comprehensible for readers of varying levels and backgrounds.
530			_aAlso available in print.
538			_aMode of access: World Wide Web
588			_aTitle from title screen.
588			_aDescription based on PDF viewed 12/21/2015.
650		0	_aCluster analysis.
650		0	_aCluster analysis _xData processing.
655		0	_aElectronic books.
695			_aAerospace electronics
695			_aAlgorithm design and analysis
695			_aAnimals
695			_aApproximation algorithms
695			_aArtificial neural networks
695			_aBibliographies
695			_aBinary trees
695			_aBioinformatics
695			_aBiology
695			_aClustering algorithms
695			_aColon
695			_aComplexity theory
695			_aConvergence
695			_aCouplings
695			_aCovariance matrix
695			_aDNA
695			_aData analysis
695			_aData mining
695			_aData structures
695			_aData visualization
695			_aDatabases
695			_aDiseases
695			_aDynamic programming
695			_aEigenvalues and eigenfunctions
695			_aFeature extraction
695			_aFrequency modulation
695			_aGenomics
695			_aHeuristic algorithms
695			_aHorses
695			_aHumans
695			_aIndexes
695			_aIterative algorithm
695			_aKernel
695			_aLead
695			_aLesions
695			_aMinimization
695			_aNearest neighbor searches
695			_aNeurons
695			_aNickel
695			_aNoise
695			_aPain
695			_aPartitioning algorithms
695			_aPolynomials
695			_aPrincipal component analysis
695			_aProteins
695			_aPrototypes
695			_aQ measurement
695			_aSampling methods
695			_aSearch problems
695			_aSections
695			_aShape
695			_aSun
695			_aSurgery
695			_aTemperature measurement
695			_aTime measurement
695			_aVectors
700	1		_aWunsch, Donald C. _4aut
710	2		_aIEEE Xplore (Online Service), _edistributor.
710	2		_aJohn Wiley & Sons _epublisher.
776	0	8	_iPrint version: _z9780470276808
830		0	_aIEEE Press Series on Computational Intelligence ; _v10
856	4	2	_3Abstract with links to resource _uhttp://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=5236612
942			_cEBK
999			_c59315 _d59315