Central Library

Clustering / (Record no. 73743)

000 -LEADER
fixed length control field 06879nam a2201297 i 4500
001 - CONTROL NUMBER
control field 5236612
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20220712205605.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 090727t20152009njua ob 001 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9780470382776
-- electronic
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
-- paper
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
-- paper
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
-- electronic
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
-- electronic
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
-- electronic
082 0# - CLASSIFICATION NUMBER
Call Number 519.5/3
082 04 - CLASSIFICATION NUMBER
Call Number 519.53
100 1# - AUTHOR NAME
Author Xu, Rui.
245 10 - TITLE STATEMENT
Title Clustering /
300 ## - PHYSICAL DESCRIPTION
Number of Pages 1 PDF (x, 358 pages) :
490 1# - SERIES STATEMENT
Series statement IEEE Press Series on Computational Intelligence ;
505 0# - FORMATTED CONTENTS NOTE
Remark 2 PREFACE -- 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# - FORMATTED CONTENTS NOTE
Remark 2 9.1. 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.
520 ## - SUMMARY, ETC.
Summary, etc This 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.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
Subject Cluster analysis.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
Subject Cluster analysis
General subdivision Data processing.
700 1# - AUTHOR 2
Author 2 Wunsch, Donald C.
856 42 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier https://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=5236612
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type eBooks
264 #1 -
-- Piscataway, New Jersey :
-- IEEE Press,
-- c2009.
264 #2 -
-- [Piscataqay, New Jersey] :
-- IEEE Xplore,
-- 2008.
336 ## -
-- text
-- rdacontent
337 ## -
-- electronic
-- isbdmedia
338 ## -
-- online resource
-- rdacarrier
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-- Title from title screen.
588 ## -
-- Description based on PDF viewed 12/21/2015.
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-- Sampling methods
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-- Search problems
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-- Sections
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-- Shape
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-- Sun
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-- Surgery
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-- Temperature measurement
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-- Time measurement
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-- Vectors
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-- Aerospace electronics
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-- Algorithm design and analysis
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-- Animals
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-- Approximation algorithms
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-- Artificial neural networks
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-- Bibliographies
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-- Binary trees
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-- Bioinformatics
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-- Biology
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-- Clustering algorithms
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-- Colon
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-- Complexity theory
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-- Convergence
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-- Couplings
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-- Covariance matrix
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-- DNA
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-- Data analysis
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-- Data mining
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-- Data structures
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-- Data visualization
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-- Databases
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-- Diseases
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-- Dynamic programming
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-- Eigenvalues and eigenfunctions
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-- Feature extraction
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-- Frequency modulation
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-- Genomics
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-- Heuristic algorithms
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-- Horses
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-- Humans
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-- Indexes
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-- Iterative algorithm
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-- Kernel
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-- Lead
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-- Lesions
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-- Minimization
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-- Nearest neighbor searches
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-- Neurons
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-- Nickel
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-- Noise
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-- Pain
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-- Partitioning algorithms
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-- Polynomials
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-- Principal component analysis
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-- Proteins
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-- Prototypes
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-- Q measurement

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