Performance Comparison of Some K-medoids Clustering Algorithms

새로운 K-medoids 군집방법 및 성능 비교

We propose a new algorithm for K-medoids clustering which runs like the K-means clustering algorithm and test several methods for selecting initial medoids. The proposed algorithm calculates similarity matrix once and uses it for finding new medoids at every iterative step. To evaluate the proposed algorithm we use real and artificial data and compare with the clustering results of other algorithms in terms of three performance measures. Experimental results show that the proposed algorithm takes the reduced time in computation with comparable performance as compared to the Partitioning Around Medoids.