Intuitive fuzzy c-means algorithm
Dong-Chul Park · 2009
Fuzzy C-means (FCM) is one of the most widely used clustering algorithms and assigns memberships to which are inversely related to the relative distance to the point prototypes that are cluster centers in the FCM model. In order to overcome the problem of outliers in data, several models including possibilistic C-means (PCM) and possibilistic-fuzzy C-means (PFCM) models have been proposed. A new model called intuitive fuzzy C-means (IFCM) model is proposed in this paper. In IFCM, a new measurement called intuition level is introduced so that the intuition level helps to alleviate the effect of noise. Several numerical examples are used for experiments to compare the clustering performance of IFCM with those of FCM, PCM, and PFCM. Results show that IFCM compares favorably to the FCM, PCM, and PFCM models. Since IFCM produces cluster prototypes less sensitive to outliers and to the selection of involved parameters than the other algorithms, IFCM is a good candidate for data clustering problems.