A parametric method for fuzzy data clustering

Given that the current era is the era of information explosion, so the clustering of existing data and information is inevitable that must be done. Since in many cases we encounter widespread uncertainties in the available data, the best way to use clustering techniques is to combine them with fuzzy mathematics. Researchers have developed a variety of clustering algorithms for data clustering, some of which have fuzzy versions. The basic idea in fuzzy clustering is to assume that each cluster is a set of elements, then by changing the definition of element membership in this set from a state where an element can only be a member of a cluster, to a state where each element can To rank different memberships within several clusters, provide more realistic categories. The fuzzy mean c clustering algorithm (FCM) is the most common fuzzy clustering method. Various forms of FCM have been proposed so far. In this paper, based on FCM algorithm and similarity criterion, an efficient and robust clustering method for fuzzy data is proposed. This is the method. The similarity criterion proposed in this paper is based on α-sections and gives the decision maker the ability to make decisions at different levels. At the end, two numerical examples and a practical example are presented to show the efficiency of the proposed method.