fpc: Fixed point clusters, clusterwise regression and discriminant plots

Fuzzy and crisp fixed point cluster analysis based on Mahalanobis distance and linear regression fixed point clusters. Semi-explorative, semi-model-based clustering methods, operating on n*p data, do not need prespecification of number of clusters, produce overlapping clusters. Symmetric and asymmetric discriminant projections separate groups optimally, used to visualize the separation of groupings, visual cluster validation. Clusterwise linear regression by normal mixture modeling. Cluster validation statistics for distance based clustering including corrected Rand index. Clusterwise cluster stability assessment. DBSCAN clustering. Interface functions for many clustering methods implemented in R. Note that the use of the package mclust (called by function prabclust) is protected by a special license, see http://www.stat.washington.edu/mclust/license.txt, particularly point 6.

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