The possibilistic C-means algorithm: Insights and recommendations

Recently, the possibilistic C-means algorithm (PCM) was proposed to address the drawbacks associated with the constrained memberships used in algorithms such as the fuzzy C-means (FCM). In this issue, Barni er al. report a difficulty they faced while applying the PCM, and note that it exhibits an undesirable tendency to converge to coincidental clusters. The purpose of this correspondence is not just to address the issues raised by Barni er al., but to go further and analytically examine the underlying principles of the PCM and the possibilistic approach, in general. We analyze the data sets used by Barni el al. and interpret the results reported by them in the light of our findings.