Refining Initial Seeds using Max Average Distance for K-Means Clustering

Shin-Won Lee; Won-Hee Lee

Refining Initial Seeds using Max Average Distance for K-Means Clustering

Shin-Won Lee, Won-Hee Lee, Journal of Internet Computing and Services, Vol. 12, No. 2, pp. 103-112, Apr. 2011
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Keywords: clustering, K-Means, initial seed

Abstract

Clustering methods is divided into hierarchical clustering, partitioning clustering, and more. If the amount of documents is huge, it takes too much time to cluster them in hierarchical clustering. In this paper we deal with K-Means algorithm that is one of partitioning clustering and is adequate to cluster so many documents rapidly and easily. We propose the new method of selecting initial seeds in K-Means algorithm. In this method, the initial seeds have been selected that are positioned as far away from each other as possible.

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Cite this article

[APA Style]

Lee, S. & Lee, W. (2011). Refining Initial Seeds using Max Average Distance for K-Means Clustering. Journal of Internet Computing and Services, 12(2), 103-112.

[IEEE Style]

S. Lee and W. Lee, "Refining Initial Seeds using Max Average Distance for K-Means Clustering," Journal of Internet Computing and Services, vol. 12, no. 2, pp. 103-112, 2011.

[ACM Style]

Shin-Won Lee and Won-Hee Lee. 2011. Refining Initial Seeds using Max Average Distance for K-Means Clustering. Journal of Internet Computing and Services, 12, 2, (2011), 103-112.