A Fuzzy Relational Clustering Algorithm with q-weighted Medoids ?
نویسندگان
چکیده
Medoids-based fuzzy relational clustering generates clusters of objects based on relational data, which records pairwise similarity or dissimilarities among objects. Compared with single-medoid based approaches, multiple-weighted medoids has shown superior performance in clustering. In this paper, we present a new version of fuzzy relational clustering in this family called fuzzy clustering with q weighted medoids (FQMdd). Based on the objective function of FQMdd, the update of memberships, prototypes of each cluster and weights are given. Compared with two existing fuzzy relational clustering approaches fuzzy c-medoids (FCMdd) and fuzzy clustering with multi-medoids (FMMdd), FQMdd can represent rich cluster-based information by multiple-weighted objects, and strengthen contribution of core objects in clusters as well. Experiments on synthetic and real-world datasets show that FQMdd mostly has higher clustering accuracy than FCMdd and FMMdd.
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