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Co-Clustering : (Record no. 116354)

000 -LEADER
fixed length control field	10273nam a22005173i 4500
001 - CONTROL NUMBER
control field	EBC1580022
003 - CONTROL NUMBER IDENTIFIER
control field	MiAaPQ
005 - DATE AND TIME OF LATEST TRANSACTION
control field	20181121171759.0
006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS--GENERAL INFORMATION
fixed length control field	m o d \|
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION
fixed length control field	cr cnu\|\|\|\|\|\|\|\|
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field	181113s2013 xx o \|\|\|\|0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number	9781118649497
--	(electronic bk.)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
Cancelled/invalid ISBN	9781118649480
035 ## - SYSTEM CONTROL NUMBER
System control number	(MiAaPQ)EBC1580022
035 ## - SYSTEM CONTROL NUMBER
System control number	(Au-PeEL)EBL1580022
035 ## - SYSTEM CONTROL NUMBER
System control number	(CaPaEBR)ebr10819261
035 ## - SYSTEM CONTROL NUMBER
System control number	(CaONFJC)MIL551636
035 ## - SYSTEM CONTROL NUMBER
System control number	(OCoLC)865655620
040 ## - CATALOGING SOURCE
Original cataloging agency	MiAaPQ
Language of cataloging	eng
Description conventions	rda
--	pn
Transcribing agency	MiAaPQ
Modifying agency	MiAaPQ
050 #4 - LIBRARY OF CONGRESS CALL NUMBER
Classification number	QA278.G683 2014eb
082 0# - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number	519.53
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name	Govaert, Gérard.
245 10 - TITLE STATEMENT
Title	Co-Clustering :
Remainder of title	Models, Algorithms and Applications.
250 ## - EDITION STATEMENT
Edition statement	1st ed.
264 #1 - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication, distribution, etc	Somerset :
Name of publisher, distributor, etc	John Wiley & Sons, Incorporated,
Date of publication, distribution, etc	2013.
264 #4 - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Date of publication, distribution, etc	©2013.
300 ## - PHYSICAL DESCRIPTION
Extent	1 online resource (252 pages)
336 ## - CONTENT TYPE
Content type term	text
Content type code	txt
Source	rdacontent
337 ## - MEDIA TYPE
Media type term	computer
Media type code	c
Source	rdamedia
338 ## - CARRIER TYPE
Carrier type term	online resource
Carrier type code	cr
Source	rdacarrier
490 1# - SERIES STATEMENT
Series statement	Computer Engineering Series
505 0# - FORMATTED CONTENTS NOTE
Formatted contents note	Cover -- Title page -- Table of Contents -- Acknowledgment -- Introduction -- I.1. Types and representation of data -- I.1.1. Binary data -- I.1.2. Categorical data -- I.1.3. Continuous data -- I.1.4. Contingency table -- I.1.5. Data representations -- I.2. Simultaneous analysis -- I.2.1. Data analysis -- I.2.2. Co-clustering -- I.2.3. Applications -- I.3. Notation -- I.4. Different approaches -- I.4.1. Two-mode partitioning -- I.4.2. Two-mode hierarchical clustering -- I.4.3. Direct or block clustering -- I.4.4. Biclustering -- I.4.5. Other structures and other aims -- I.5. Model-based co-clustering -- I.6. Outline -- Chapter 1. Cluster Analysis -- 1.1. Introduction -- 1.2. Miscellaneous clustering methods -- 1.2.1. Hierarchical approach -- 1.2.2. The k-means algorithm -- 1.2.3. Other approaches -- 1.3. Model-based clustering and the mixture model -- 1.4. EM algorithm -- 1.4.1. Complete data and complete-data likelihood -- 1.4.2. Principle -- 1.4.3. Application to mixture models -- 1.4.4. Properties -- 1.4.5. EM: an alternating optimization algorithm -- 1.5. Clustering and the mixture model -- 1.5.1. The two approaches -- 1.5.2. Classification likelihood -- 1.5.3. The CEM algorithm -- 1.5.4. Comparison of the two approaches -- 1.5.5. Fuzzy clustering -- 1.6. Gaussian mixture model -- 1.6.1. The model -- 1.6.2. CEM algorithm -- 1.6.3. Spherical form, identical proportions and volumes -- 1.6.4. Spherical form, identical proportions but differing volumes -- 1.6.5. Identical covariance matrices and proportions -- 1.7. Binary data -- 1.7.1. Binary mixture model -- 1.7.2. Parsimonious model -- 1.7.3. Examples of application -- 1.8. Categorical variables -- 1.8.1. Multinomial mixture model -- 1.8.2. Parsimonious model -- 1.9. Contingency tables -- 1.9.1. MNDKI2 algorithm -- 1.9.2. Model-based approach -- 1.9.3. Illustration -- 1.10. Implementation.
505 8# - FORMATTED CONTENTS NOTE
Formatted contents note	1.10.1. Choice of model and of the number of classes -- 1.10.2. Strategies for use -- 1.10.3. Extension to particular situations -- 1.11. Conclusion -- Chapter 2. Model-Based Co-Clustering -- 2.1. Metric approach -- 2.2. Probabilistic models -- 2.3. Latent block model -- 2.3.1. Definition -- 2.3.2. Link with the mixture model -- 2.3.3. Log-likelihoods -- 2.3.4. A complex model -- 2.4. Maximum likelihood estimation and algorithms -- 2.4.1. Variational EM approach -- 2.4.2. Classification EM approach -- 2.4.3. Stochastic EM-Gibbs approach -- 2.5. Bayesian approach -- 2.6. Conclusion and miscellaneous developments -- Chapter 3. Co-Clustering of Binary and Categorical Data -- 3.1. Example and notation -- 3.2. Metric approach -- 3.3. Bernoulli latent block model and algorithms -- 3.3.1. The model -- 3.3.2. Model identifiability -- 3.3.3. Binary LBVEM and LBCEM algorithms -- 3.4. Parsimonious Bernoulli LBMs -- 3.5. Categorical data -- 3.6. Bayesian inference -- 3.7. Model selection -- 3.7.1. The integrated completed log-likelihood (ICL) -- 3.7.2. Penalized information criteria -- 3.8. Illustrative experiments -- 3.8.1. Townships -- 3.8.2. Mero -- 3.9. Conclusion -- Chapter 4. Co-Clustering of Contingency Tables -- 4.1. Measures of association -- 4.1.1. Phi-squared coefficient -- 4.1.2. Mutual information -- 4.2. Contingency table associated with a couple of partitions -- 4.2.1. Associated distributions -- 4.2.2. Associated measures of association -- 4.3. Co-clustering of contingency table -- 4.3.1. Two equivalent approaches -- 4.3.2. Parameter modification of criteria -- 4.3.3. Co-clustering with the phi-squared coefficient -- 4.3.4. Co-clustering with the mutual information -- 4.4. Model-based co-clustering -- 4.4.1. Block model for contingency tables -- 4.4.2. Poisson latent block model -- 4.4.3. Poisson LBVEM and LBCEM algorithms.
505 8# - FORMATTED CONTENTS NOTE
Formatted contents note	4.5. Comparison of all algorithms -- 4.5.1. CROKI2 versus CROINFO -- 4.5.2. CROINFO versus Poisson LBCEM -- 4.5.3. Poisson LBVEM versus Poisson LBCEM -- 4.5.4. Behavior of CROKI2, CROINFO, LBCEM and LBVEM -- 4.6. Conclusion -- Chapter 5. Co-Clustering of Continuous Data -- 5.1. Metric approach -- 5.1.1. Measure of information -- 5.1.2. Summarized data associated with partitions -- 5.1.3. Objective function -- 5.1.4. CROEUC algorithm -- 5.2. Gaussian latent block model -- 5.2.1. The model -- 5.2.2. Gaussian LBVEM and LBCEM algorithms -- 5.2.3. Parsimonious Gaussian latent block models -- 5.3. Illustrative example -- 5.4. Gaussian block mixture model -- 5.4.1. The model -- 5.4.2. GBEM algorithm -- 5.5. Numerical experiments -- 5.5.1. GBEM versus CROEUC and EM -- 5.5.2. Effect of the size of data -- 5.6. Conclusion -- Bibliography -- Index.
520 ## - SUMMARY, ETC.
Summary, etc	Cluster or co-cluster analyses are important tools in a variety of scientific areas. The introduction of this book presents a state of the art of already well-established, as well as more recent methods of co-clustering. The authors mainly deal with the two-mode partitioning under different approaches, but pay particular attention to a probabilistic approach. Chapter 1 concerns clustering in general and the model-based clustering in particular. The authors briefly review the classical clustering methods and focus on the mixture model. They present and discuss the use of different mixtures adapted to different types of data. The algorithms used are described and related works with different classical methods are presented and commented upon. This chapter is useful in tackling the problem of co-clustering under the mixture approach. Chapter 2 is devoted to the latent block model proposed in the mixture approach context. The authors discuss this model in detail and present its interest regarding co-clustering. Various algorithms are presented in a general context. Chapter 3 focuses on binary and categorical data. It presents, in detail, the appropriated latent block mixture models. Variants of these models and algorithms are presented and illustrated using examples. Chapter 4 focuses on contingency data. Mutual information, phi-squared and model-based co-clustering are studied. Models, algorithms and connections among different approaches are described and illustrated. Chapter 5 presents the case of continuous data. In the same way, the different approaches used in the previous chapters are extended to this situation. Contents 1. Cluster Analysis. 2. Model-Based Co-Clustering. 3. Co-Clustering of Binary and Categorical Data. 4. Co-Clustering of Contingency Tables. 5. Co-Clustering of Continuous Data. About the Authors Gérard Govaert is
520 8# - SUMMARY, ETC.
Summary, etc	Professor at the University of Technology of Compiègne, France. He is also a member of the CNRS Laboratory Heudiasyc (Heuristic and diagnostic of complex systems). His research interests include latent structure modeling, model selection, model-based cluster analysis, block clustering and statistical pattern recognition. He is one of the authors of the MIXMOD (MIXtureMODelling) software. Mohamed Nadif is Professor at the University of Paris-Descartes, France, where he is a member of LIPADE (Paris Descartes computer science laboratory) in the Mathematics and Computer Science department. His research interests include machine learning, data mining, model-based cluster analysis, co-clustering, factorization and data analysis. Cluster Analysis is an important tool in a variety of scientific areas. Chapter 1 briefly presents a state of the art of already well-established as well more recent methods. The hierarchical, partitioning and fuzzy approaches will be discussed amongst others. The authors review the difficulty of these classical methods in tackling the high dimensionality, sparsity and scalability. Chapter 2 discusses the interests of coclustering, presenting different approaches and defining a co-cluster. The authors focus on co-clustering as a simultaneous clustering and discuss the cases of binary, continuous and co-occurrence data. The criteria and algorithms are described and illustrated on simulated and real data. Chapter 3 considers co-clustering as a model-based co-clustering. A latent block model is defined for different kinds of data. The estimation of parameters and co-clustering is tackled under two approaches: maximum likelihood and classification maximum likelihood. Hard and soft algorithms are described and applied on simulated and real data. Chapter 4 considers co-clustering as a matrix approximation. The trifactorization
520 8# - SUMMARY, ETC.
Summary, etc	approach is considered and algorithms based on update rules are described. Links with numerical and probabi.
588 ## - SOURCE OF DESCRIPTION NOTE
Source of description note	Description based on publisher supplied metadata and other sources.
590 ## - LOCAL NOTE (RLIN)
Local note	Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2018. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element	Cluster analysis.
655 #4 - INDEX TERM--GENRE/FORM
Genre/form data or focus term	Electronic books.
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name	Nadif, Mohamed.
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Display text	Print version:
Main entry heading	Govaert, Gérard
Title	Co-Clustering : Models, Algorithms and Applications
Place, publisher, and date of publication	Somerset : John Wiley & Sons, Incorporated,c2013
International Standard Book Number	9781118649480
797 2# - LOCAL ADDED ENTRY--CORPORATE NAME (RLIN)
Corporate name or jurisdiction name as entry element	ProQuest (Firm)
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
Uniform title	Computer Engineering Series
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier	<a href="https://ebookcentral.proquest.com/lib/buse-ebooks/detail.action?docID=1580022">https://ebookcentral.proquest.com/lib/buse-ebooks/detail.action?docID=1580022</a>
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