  In some complicated datasets, due to the presence of noisy data points and
outliers, cluster validity indices can give conflicting results in determining
the optimal number of clusters. This paper presents a new validity index for
fuzzy-possibilistic c-means clustering called Fuzzy-Possibilistic (FP) index,
which works well in the presence of clusters that vary in shape and density.
Moreover, FPCM like most of the clustering algorithms is susceptible to some
initial parameters. In this regard, in addition to the number of clusters, FPCM
requires a priori selection of the degree of fuzziness and the degree of
typicality. Therefore, we presented an efficient procedure for determining
their optimal values. The proposed approach has been evaluated using several
synthetic and real-world datasets. Final computational results demonstrate the
capabilities and reliability of the proposed approach compared with several
well-known fuzzy validity indices in the literature. Furthermore, to clarify
the ability of the proposed method in real applications, the proposed method is
implemented in microarray gene expression data clustering and medical image
segmentation.
