000 06825nam a2201297 i 4500
001 5236612
003 IEEE
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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