An Improved K-Medoids Clustering Algorithm

Abstract:

Because of the traditional K-medoids clustering algorithm the initial clustering center sensitive, the global search ability is poor, easily trapped into local optimal and slow convergent speed; therefore, this article proposes an improved K-medoids clustering algorithm. Differential evolution is a kind of heuristic global search technology population, has strong robustness. Combined with K-medoids clustering algorithm efficiency and the global optimization ability of DE algorithm, not only can effectively overcome the detects of the K-medoids clustering algorithm, but also can raise the global search capability, short the convergence time, effectively improve the clustering quality. Finally, the algorithm is verified stability and robustness by simulation.

• Periodical: Advanced Materials Research (Volumes 562-564)
• Pages: 2106-2110
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.562-564.2106
• Online since: August 2012
• Authors: Ying Meng, Ke Luo, Jian Hua Liu
• Keywords: Cluster Quality, Differential Evolution, Global Optimization, K-Medoids Algorithm

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