An Introduction to Classification and Clustering -- Reasons for classifying -- Numerical methods of classification -- cluster analysis -- What is a cluster? -- Examples of the use of clustering -- Visualizing Clusters -- Detecting clusters in one or two dimensions -- Visualizing clusters in data sets with more than three variables -- Multidimensional scaling -- Measurement of Proximity -- Similarity measures for categorical data -- Dissimilarity and distance measures for continuous data -- Similarity measures for data containing both continuous and categorical variables -- Inter-group proximity measures -- Weighting variables -- Standardization -- Choice of proximity measure -- Missing data values -- Hierarchical Clustering -- Agglomerative methods -- Divisive methods -- Applying the hierarchical clustering process -- Applications of hierarchical methods -- Optimization Clustering Techniques -- Clustering criteria derived from the dissimilarity matrix -- Clustering criteria derived from continuous data -- Optimization algorithms -- Choosing the number of clusters -- Applications of optimization methods -- Finite Mixture Densities as Models for Cluster Analysis -- Finite mixture densities -- Other finite mixture densities -- Tests for the number of components -- Applications of finite mixture densities and classification likelihood -- Miscellaneous Clustering Methods -- Density search clustering techniques -- Techniques which allow overlapping clusters -- Direct clustering of data matrices -- Clustering with constraints
