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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...
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Wiley-ISTE,
2013.
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| Colección: | FOCUS Series.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- 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 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.
- 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.
- 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.
- 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.