Large-scale fuzzy multiple-medoid clustering method

Abstract

As one of feasible clustering techniques for large-scale data, incremental fuzzy clustering, which copes with data in chunks, has triggered more attentions in recent years. The existing methods, such as online fuzzy C -medoids (OFCMd) and history-based online fuzzy C -medoids (HOFCMd), employ only one medoid to represent each cluster in chunks. Due to the fact that the representativeness of the one-medoid modality is sometimes unsatisfactory, a novel large-scale fuzzy multiple-medoid clustering (LS-FMMdC) method is presented to strengthen the clustering effectiveness for large-scale data. The performance of the proposed method is verified by comparing LS-FMMdC with OFCMd and HOFCMd on both synthetic and real-life large-scale data sets.