A New Suppression-based Possibilistic Fuzzy c-means Clustering Algorithm

Possibilistic fuzzy c-means (PFCM) is one of the most widely used clustering algorithm that solves noise sensitivity problem Fuzzy (FCM) and coincident clusters possibilistic (PCM). Though PFCM a highly reliable but efficiency can be further improved by introducing concept suppression. Suppression-based algorithms employ winner non-winner based suppression technique on datasets, helping in performing better classification real-world datasets into clusters. In this paper, we propose suppression-based (SPFCM) for process clustering. The paper explores performance proposed methodology number misclassifications various real synthetic it found to perform than other techniques sequel, i.e., normal as well algorithms. SPFCM more efficiently converges faster compared techniques.