Resampling for Fuzzy Clustering

Resampling methods are among the best approaches to determine the number of clusters in prototype-based clustering. The core idea is that with the right choice for the number of clusters basically the same cluster structures should be obtained from subsamples of the given data set, while a wrong choice should produce considerably varying cluster structures. In this paper I give a brief overview how such resampling approaches can be transferred to fuzzy and probabilistic clustering.