An extension of possibilistic fuzzy c-means with regularization
Younghwan Namkoong, Gyeongyong Heo, Young Woon Woo · 2010
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.