An extension of possibilistic fuzzy c-means with regularization

Fuzzy c-means (FCM) and possibilistic c-means (PCM) are the two most well-known clustering algorithms in fuzzy clustering area, and have been applied in many areas with their original or modified forms. However, FCM´s noise sensitivity problem and PCM´s overlapping cluster problem are also well known. Recently there have been several attempts to combine both of them to mitigate these problems and possibilistic fuzzy c-means (PFCM) showed promising results. In this paper, we propose a modified PFCM using regularization to reduce noise sensitivity in PFCM further. Regularization is a well-known technique to make a solution space smooth and an algorithm noise insensitive. The proposed algorithm, PFCM with regularization (PFCM-R), takes advantage of regularization and further reduce the effect of noise. Experimental results are given and show that PFCM-R is better than existing methods in noisy conditions.