Robust FCMdd-based Linear Clustering for Relational Data with Alternative c-Means Criterion
Takashi Yamamoto, Katsuhiro Honda, Akira Notsu, H. Ichihashi · 2011
Relational clustering is actively studied in data mining, in which intrinsic data structure is summarized into cluster structure. A linear fuzzy clustering model based on Fuzzy c-Medoids (FCMdd) is proposed for extracting intrinsic local linear substructures from relational data. Alternative Fuzzy c-Means (AFCM) is an extension of Fuzzy c-means, in which a modified distance measure instead of the conventional Euclidean distance is used based on the robust M-estimation concept. In this paper, the FCMdd-based linear clustering model is further modified in order to extract linear substructure from relational data including outliers, using a pseudo-M-estimation procedure with a weight function for the modified distance measure in AFCM.