Affiliations: School of Digital Media, Jiangnan University, Wuxi, Jiangsu, P.R. China
Correspondence:
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Corresponding author. Pengjiang Qian, School of Digital Media, Jiangnan University, Wuxi, Jiangsu, P.R. China. Tel.: +86 137 71510961; Fax: +86 510 85913570; E-mail: [email protected].
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.
Keywords: Fuzzy C-medoids, incremental clustering, data chunk, multiple medoids, large-scale data
