Cluster evaluation measures

Several techniques can be used to score the quality of a clustering algorithm.

AIC
[1]

BIC

[1]
C-index
[2]
Gamma
[2]
G(+)
[2]
Hybride centroid similarity
[3]
Hybride pairwise similarity
[3]
Min max cut
[3]
Point biserial
[2]
Sum of average pairwise similarities
[3]
Sum of centroid similarities
[3]
Sum of squared errors
[3]
Tau implemented
[2]
Trace scatter matrix
[3]
WB
[2]

References
1. W. Li.: "Applications of akaike and bayesin information criteria in epidemiology, linkage analysis, microarray data analysis, and dna sequence analysis", Technical report, Lab of Statistical Genetics, Rockerfeller University.
2. G. W. Milligan: "A monte carlo study of thirty internal criterion measures for cluster analysis", Psychometrika, 46:187-199, 1981.
3. Y. Zhao and G. Karypis: "Criterion functions for document clustering", Technical report, Department of Computer Science, University of Minnesota / Army HPC Research Center, 2002.