Efficient search approaches for K-medoids-based algorithms
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In this paper, the concept of previous medoid index is introduced. The utilization of memory for efficient medoid search is also presented. We propose a hybrid search approach for the problem of nearest neighbor search. The hybrid search approach combines the previous medoid index, the utilization of memory, the criterion of triangular inequality elimination and the partial distance search. The proposed hybrid search approach is applied to the k-medoids-based algorithms. Experimental results based on Gauss-Markov source, curve data set and elliptic clusters demonstrate that the proposed algorithm applied to the CLARANS algorithm may reduce the number of distance calculations from 88.4% to 95.2% with the same average distance per object compared with CLARANS. The proposed hybrid search approach can also be applied to nearest neighbor searching and the other clustering algorithms.