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Cluster Analysis, 4th Edition
Comment from the Stata technical groupCluster Analysis, Fourth Edition, by Brian S. Everitt, Sabine Landau, and Morven Leese, is a popular, well-written introduction and reference to cluster analysis. The book introduces the topic and discusses a variety of cluster-analysis methods. The fourth edition contains a lot of practical information, for example, how best to visualize clusters, how (and if) to select and transform variables, how to choose among the clustering methods, and how to compare the results of different cluster analyses. To illustrate the discussion, the book contains several examples. Table of contentsPreface
1. An Introduction to Classification and Clustering
1.1 Introduction
1.2 Reasons for classifying 1.3 Numerical methods of classification–cluster analysis 1.4 What is a cluster? 1.5 Examples of the use of clustering 1.6 Summary 2. Visualizing Clusters
2.1 Introduction
2.2 Detecting clusters in one or two dimensions 2.3 Visualizing clusters in data sets with more than three variables 2.4 Multidimensional scaling 2.5 Summary 2.6 Exercises 3. Measurement of Proximity
3.1 Introduction
3.2 Similarity measures for categorical data 3.3 Dissimilarity and distance measures for continuous data 3.4 Similarity measures for data containing both continuous and categorical variables 3.5 Inter-group proximity measures 3.6 Weighting variables 3.7 Standardization 3.8 Choice of proximity measure 3.9 Missing data values 3.10 Summary 4. Hierarchical Clustering
4.1 Introduction
4.2 Agglomerative methods 4.3 Divisive methods 4.4 Applying the hierarchical clustering process 4.5 Applications of hierarchical methods 4.6 Summary 5. Optimization Clustering Techniques
5.1 Introduction
5.2 Clustering criteria derived from the dissimilarity matrix
5.3 Clustering criteria derived from continuous data 5.4 Optimization algorithms 5.5 Choosing the number of clusters 5.6 Applications of optimization methods 5.7 Summary 6. Finite Mixture Densities as Models for Cluster Analysis
6.1 Introduction
6.2 Finite mixture densities 6.3 Other finite mixture densities 6.4 Tests for the number of components 6.5 Applications of finite mixture densities and classification likelihood 6.6 Summary 7. Miscellaneous Clustering Methods
7.1 Introduction
7.2 Density search clustering techniques 7.3 Techniques which allow overlapping clusters 7.4 Direct clustering of data matrices 7.5 Clustering with constraints 7.6 Fuzzy clustering 7.7 Clustering and artificial neural networks 7.8 Summary 8. Some Final Comments and Guidelines
8.1 Introduction
8.2 Using clustering techniques in practice 8.3 Testing for absence of structure 8.4 Methods for comparing cluster solutions 8.5 Internal cluster quality, influence and robustness 8.6 Graphical interpretation 8.7 Illustrative examples 8.8 Summary Appendix: Software for Cluster Analysis
A.1 Introduction
A.2 Statistical packages incorporating cluster analysis A.3 Software for mixture modelling A.4 Software for non-standard clustering methods Bibliography
Index
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