An Implementation of K-Means Algorithm Improving Cluster Centroids Decision Methodologies


The KIPS Transactions:PartB , Vol. 11, No. 7, pp. 867-874, Dec. 2004
10.3745/KIPSTB.2004.11.7.867,   PDF Download:

Abstract

K-Means algorithm is a non-hierarchical (plat) and reassignment techniques and iterates algorithm steps on the basis of K cluster centroids until the clustering results converge into K clusters. In its nature, K-Means algorithm has characteristics which make different results depending on the initial and new centroids. In this paper, we propose the modified K-Means algorithm which improves the initial and new centroids decision methodologies. By evaluating the performance of two algorithms using the 16 weighting scheme of SMART system, the modified algorithm showed 20% better results on recall and F-measure than those of K-Means algorithm, and the document clustering results are quite improved.


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Cite this article
[IEEE Style]
S. W. Lee, H. J. Oh, D. U. An, S. J. Jeong, "An Implementation of K-Means Algorithm Improving Cluster Centroids Decision Methodologies," The KIPS Transactions:PartB , vol. 11, no. 7, pp. 867-874, 2004. DOI: 10.3745/KIPSTB.2004.11.7.867.

[ACM Style]
Shin Won Lee, Hyung Jin Oh, Dong Un An, and Seong Jong Jeong. 2004. An Implementation of K-Means Algorithm Improving Cluster Centroids Decision Methodologies. The KIPS Transactions:PartB , 11, 7, (2004), 867-874. DOI: 10.3745/KIPSTB.2004.11.7.867.