The MinMax Fuzzy C-Means

Fuzzy c-means (FCM) is one of the most popular fuzzy clustering methods and it is used in various applications in computer science. Most clustering methods including FCM, suffer from bad initialization problem. If initial cluster centers (membership degree initialization in FCM) are not selected appropriately, it may yield poor results. In this paper we propose a method called MinMax FCM to overcome this problem. A new objective function is designed in MinMax FCM to this aim. We use maximum variance of clusters as objective function. In this regard, high-variance clusters are penalized. We compare MinMax FCM with FCM in terms of sum of clusters’ variances, maximum variance of clusters, and execution time using a number of UCI datasets.