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. Discriminant projections separate
groups optimally, used to visualize the separation of
groupings. Corresponding plot methods. Clusterwise linear
regression by normal mixture modeling.

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. Corresponding plot methods. Clusterwise linear
regression by normal mixture modeling. Cluster validation statistics
for distance based clustering.

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. Corresponding plot methods. Clusterwise linear
regression by normal mixture modeling. Cluster validation statistics
for distance based clustering. Note: If you use an R-version older
than 1.9.0, you will need packages lqs and mva installed.

mclust, trimcluster, prabclus, cluster, class
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. Clusterwise cluster stability assessment.
DBSCAN clustering. Interface functions for many clustering methods
implemented in R.