A K-means-like Algorithm for K-medoids Clustering

  • 이종석 (포항공과대학교 기계산업공학부) ;
  • 박해상 (포항공과대학교 기계산업공학부) ;
  • 전치혁 (포항공과대학교 기계산업공학부)
  • Published : 2005.10.29

Abstract

Clustering analysis is a descriptive task that seeks to identify homogeneous groups of objects based on the values of their attributes. In this paper we propose a new algorithm for K-medoids clustering which runs like the K-means algorithm. The new algorithm calculates distance matrix once and uses it for finding new medoids at every iterative step. We evaluate the proposed method using real and synthetic data and compare with the results of other algorithms. The proposed algorithm takes reduced time in computation and better performance than others.

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