Fuzzy C-Medoids Clustering Based on Interval Type-2 Inituitionistic Fuzzy Sets

Abstract

For clustering problems, each data sample has the potential to belong to many different clusters depending on the similarity. However, besides the degree of similarity and non-similarity, there is a degree of hesitation in determining whether or not a data sample belongs to a defined cluster. Besides the fuzzy c-means algorithm (FCM), another popular algorithm is fuzzy C-medoids clustering (FCMdd). FCMdd chooses several existing objects as the cluster centroids, while FCM considers the samples’ weighted average to be the cluster centroid. This subtle difference causes the FCMdd is more resistant to interference than FCM. Since noise samples will more easily affect the center of centroids of the FCM, it is easier to create clustering results with great accuracy. In this study, we proposed a method for extending the fuzzy c-medoids clustering based on interval type-2 intuitionistic fuzzy sets, named the interval type-2 intuitionistic fuzzy c-medoids clustering algorithm (IT2IFCMdd). With this combination, the proposed algorithm can take advantage of both the fuzzy c-medoids clustering (FCMdd) method and the interval type-2 intuitionistic fuzzy sets applied to the clustering problem. Experiments performed on data sets commonly used in machine learning show that the proposed method gives better clustering results in most experimental cases.