A Weighted k-Medoids Clustering Algorithm Based on Granular Computing

Because of the problems that the fast k-Medoids clustering algorithm does not consider the weight of each attribute and the initial clustering center may be in the same cluster, this paper proposes a weighted $\boldsymbol{k}$-Medoids clustering algorithm based on granular computing. Firstly, the hierarchical structure in the fuzzy quotient space theory is introduced to define the decision attribute of the sample under each granularity, and the computing method of sample attribute weight is defined by the attributes of the sample set itself and the definition of attribute importance in the rough set model. Secondly, the sample similarity function is defined by the attribute weight coefficient, and the attribute weight is integrated into the similarity of the fast k-Medoids clustering algorithm to quantitatively define the importance of each sample's attribute. Finally, from the prospective view of granular computing, the samples are clustered according to the above similarity function, and the original clustering centers are initialized by K cluster centers with long distance. The experimental results on machine learning datasets UCI show that the proposed weighted k-Medoids clustering algorithm based on granular computing greatly improves the accuracy of clustering.