Clustering and Distances

distm Distance matrix between two data sets. more routines
emclust Expectation – maximization clustering
proxm Proximity mapping and kernel construction
hclust Hierarchical clustering
kcentres k-centres clustering
kmeans k-means clustering
modeseek Clustering by modeseeking
mds Non-linear mapping by multi-dimensional scaling (Sammon)
mds_cs Linear mapping by classical scaling
mds_init Initialisation of multi-dimensional scaling
mds_stress Dissimilarity of distance matrices

elements: datasets datafiles cells and doubles mappings classifiers mapping types.
operations: datasets datafiles cells and doubles mappings classifiers stacked parallel sequential dyadic.
user commands: datasets representation classifiers evaluation clustering examples support routines.
introductory examples: Introduction Scatterplots Datasets Datafiles Mappings Classifiers Evaluation Learning curves Feature curves Dimension reduction Combining classifiers Dissimilarities.
advanced examples.

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