K-medoids Clustering Algorithms with Optimized Initial Seeds by Granular Computing

To overcome the defects of fast K- medoids clustering algorithm which may choose the initial seeds in a same cluster for different clusters and the arbitrary of granular computing based K-medoids clustering algorithm in determining the threshold to construct the defuzzy similarity matrix, this paper proposes two new K-medoids clustering algorithms with optimized initial seeds by granular computing. This proposed algorithms combine granular computing with max-min distance means to choose the optimal initial seeds, so that the K instances in dense area and apart from each other are selected as initial seeds, and adopt the mean similarity between instances as the threshold to construct the defuzzy similarity matrix. This paper tests the proposed algorithms on the synthetically generated datasets and the datasets from UCI machine learning repository. The experimental results evaluated in terms of clustering accuracy and Adjusted Rand Index etc. demonstrate that the proposed K-medoids algorithms are superior to the traditional K- medoids algorithm, the fast K- medoids algorithm and the previous K- medoids clustering algorithm based on granular computing.