Clustering is one of the most important task in pattern recognition. For most of partitional clustering algorithms, a partition that represents as much as possible the structure of the data is generated. In this paper, we adress the problem of finding the optimal number of clusters from data. This can be done by introducing an index which evaluates the validity of the generated fuzzy c-partition. We propose to use a criterion based on the fuzzy combination of membership values which quantifies the $l$-order overlap and the intercluster separation of a given pattern.