Feature Weighted Cutset-type Possibilistic Fuzzy C-Means Clustering Algorithm

Abstract

Possibilistic fuzzy c-means clustering (PFCM) algorithm improves the noise sensitivity of the fuzzy c-means clustering (FCM) algorithm and the serious coincident clustering phenomenon of the possibilistic c-means clustering (PCM) algorithm. However, the Euclidean distance of the PFCM treats all features equally in the clustering process and doesn't consider the imbalance between sample features, resulting in low clustering accuracy. Furthermore, as the number of clusters increases, the PFCM still has a partially coincident clustering problem due to the lack of between-class relationships of possibilistic memberships. Therefore, a feature weighted cutset-type possibilistic fuzzy c-means clustering (FW-C-PFCM) algorithm is proposed by introducing the cutset theory and feature weights into the PFCM in this paper. Firstly, the proposed FW-C-PFCM introduces a feature weighted parameter in the objective function, and different features should take different weight values according to the distribution of samples. Secondly, the cutset theory is utilized to divide the data into inner core and outer core regions, and some possibilistic memberships inside each cluster core are modified in the iterative process. Finally, the experiments on synthetic multi-class datasets and the Iris dataset show that the FW-C-PFCM improves the partially coincident clustering phenomenon and overcomes the imbalance between sample features. The comparative experiments on a color image also indicate that the FW-C-PFCM enhances the segmentation accuracy and generates better clustering results than PFCM.