Co-Clustering.

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 approach...

Descripción completa

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Govaert, G?rard
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Wiley-ISTE, 2013.
Colección:FOCUS Series.
Temas:
Cluster analysis.
Classification automatique (Statistique)
Cluster analysis
Acceso en línea:Texto completo

MARC

LEADER 00000cam a2200000Ma 4500
001 EBOOKCENTRAL_ocn865655620
003 OCoLC
005 20240329122006.0
006 m o d
007 cr |n|||||||||
008 131220s2013 xx o 000 0 eng d
040 |a IDEBK  |b eng  |e pn  |c IDEBK  |d EBLCP  |d MHW  |d DEBSZ  |d MEAUC  |d OCLCQ  |d AZU  |d OCLCO  |d OCLCF  |d OCLCQ  |d ZCU  |d MERUC  |d OCLCQ  |d ICG  |d AU@  |d OCLCQ  |d WYU  |d DKC  |d OCLCQ  |d OCLCO  |d OCLCQ  |d OCLCO  |d OCLCL 
019 |a 1059074922 
020 |a 1306203856  |q (ebk) 
020 |a 9781306203852  |q (ebk) 
020 |a 9781118649497 
020 |a 1118649494 
020 |a 1848214731 
020 |a 9781848214736 
029 1 |a DEBSZ  |b 399573100 
029 1 |a DEBSZ  |b 425887081 
029 1 |a DEBSZ  |b 431583404 
029 1 |a DEBSZ  |b 449403785 
035 |a (OCoLC)865655620  |z (OCoLC)1059074922 
037 |a 551636  |b MIL 
050 4 |a QA278 .G384 2013 
082 0 4 |a 519.53 
049 |a UAMI 
100 1 |a Govaert, G?rard. 
245 1 0 |a Co-Clustering. 
260 |b Wiley-ISTE,  |c 2013. 
300 |a 1 online resource 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
490 1 |a FOCUS Series 
520 |a 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 adapt. 
505 0 |a 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. 
505 8 |a Chapter 1. Cluster Analysis1.1. 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. 
505 8 |a 1.5.5. Fuzzy clustering1.6. 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 |a 1.10.1. Choice of model and of the number of classes1.10.2. 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. 
505 8 |a Chapter 3. Co-Clustering of Binary and Categorical Data3.1. 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. 
588 0 |a Print version record. 
546 |a English. 
590 |a ProQuest Ebook Central  |b Ebook Central Academic Complete 
650 0 |a Cluster analysis. 
650 6 |a Classification automatique (Statistique) 
650 7 |a Cluster analysis  |2 fast 
758 |i has work:  |a Co-clustering (Text)  |1 https://id.oclc.org/worldcat/entity/E39PCFy4fWc3MWTrfxX7CX6c4C  |4 https://id.oclc.org/worldcat/ontology/hasWork 
776 0 8 |i Print version:  |z 9781306203852 
830 0 |a FOCUS Series. 
856 4 0 |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=1580022  |z Texto completo 
938 |a ProQuest Ebook Central  |b EBLB  |n EBL1580022 
938 |a ProQuest MyiLibrary Digital eBook Collection  |b IDEB  |n cis27030038 
994 |a 92  |b IZTAP 

Ejemplares similares