Package 'fpc' - open source solution for an Internet free, intelligent ...

24 cluster.stats
pearsongamma correlation between distances and a 0-1-vector where 0 means same cluster, 1
means different clusters. "Normalized gamma" in Halkidi et al. (2001).
dunn minimum separation / maximum diameter. Dunn index, see Halkidi et al. (2002).
dunn2
entropy
wb.ratio
ch
\
Note
cwidegap
widestgap
sindex
minimum average dissimilarity between two cluster / maximum average within
cluster dissimilarity, another version of the family of Dunn indexes.
entropy of the distribution of cluster memberships, see Meila(2007).
average.within/average.between.
Calinski and Harabasz index (Calinski and Harabasz 1974, optimal in Milligan
and Cooper 1985; generalised for dissimilarites in Hennig and Liao 2010)
vector of widest within-cluster gaps.
widest within-cluster gap.
separation index, see argument sepindex.
corrected.rand corrected Rand index (if alt.clustering has been specified), see Gordon (1999,
p. 198).
vi
variation of information (VI) index (if alt.clustering has been specified), see
Meila (2007).
Because cluster.stats processes a full dissimilarity matrix, it isn’t suitable for large data sets.
You may consider distcritmulti in that case.
Author(s)
Christian Hennig http://www.homepages.ucl.ac.uk/~ucakche/
References
Calinski, T., and Harabasz, J. (1974) A Dendrite Method for Cluster Analysis, Communications in
Statistics, 3, 1-27.
Gordon, A. D. (1999) Classification, 2nd ed. Chapman and Hall.
Halkidi, M., Batistakis, Y., Vazirgiannis, M. (2001) On Clustering Validation Techniques, Journal
of Intelligent Information Systems, 17, 107-145.
Hennig, C. and Liao, T. (2010) Comparing latent class and dissimilarity based clustering for mixed
type variables with application to social stratification. Research report no. 308, Department of
Statistical Science, UCL. http://www.ucl.ac.uk/Stats/research/reports/psfiles/rr308.
pdf. Revised version accepted for publication by Journal of the Royal Statistical Society Series C.
Kaufman, L. and Rousseeuw, P.J. (1990). "Finding Groups in Data: An Introduction to Cluster
Analysis". Wiley, New York.
Meila, M. (2007) Comparing clusteringsan information based distance, Journal of Multivariate
Analysis, 98, 873-895.
Milligan, G. W. and Cooper, M. C. (1985) An examination of procedures for determining the number
of clusters. Psychometrika, 50, 159-179.