A New Approach for Semi-supervised Fuzzy Clustering with Multiple Fuzzifiers

Data clustering is the process of dividing data elements into different clusters in which elements in one cluster have more similarity than those in other clusters. Semi-supervised fuzzy clustering methods are used in various applications. The available methods are based on fuzzy C-Mean, kernel function, weight function and adaptive function. The fuzzification coefficient is an important factor that affects to the performance in these methods. In this paper, we propose the improvements of semi-supervised standard fuzzy C-Mean clustering (SSFCM) by using multiple fuzzifiers to increase clusters quality. Two proposed models, named as MCSSFC-P and MCSSFC-C, use different fuzzifiers for each data point and for each cluster, respectively, which are established in a form of optimal problems. The values of fuzzifiers are updated to get the best values of objective functions. Evaluations on different datasets are performed. The numerical results show the higher performance of our model than some related models.