An Incremental Multi-Centroid, Multi-Run Sampling Scheme for k-medoids-based algorithms
Shu‐Chuan Chu, John F. Roddick, Jeng‐Shyang Pan · 2002
Data clustering has become an important task for discovering significant patterns and characteristics in large spatial databases. The Multi-Centroid, Multi-Run Sampling Scheme (MCMRS) has been shown to be effective in improving the k-medoids-based clustering algorithms in our previous work. In this paper, a more advanced sampling scheme termed the Incremental (IMCMRS) is proposed for k-medoids-based clustering algorithms. Experimental results demonstrate the proposed scheme can not only reduce by more than 80% computation time but also reduce the average distance per object compared with CLARA and CLARANS. IMCMRS is also superior to MCMRS.