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Selection of Cluster Hierarchy Depth and Initial Centroids in Hierarchical Clustering using K-Means Algorithm

  • Journal of the Korean Society for Information Management
  • Abbr : JKOSIM
  • 2004, 21(4), pp.173~186
  • DOI : 10.3743/KOSIM.2004.21.4.173
  • Publisher : 한국정보관리학회
  • Research Area : Interdisciplinary Studies > Library and Information Science
  • Received : November 17, 2004
  • Accepted : December 18, 2004
  • Published : December 30, 2004

Lee, Shinwon 1 An Dong Un 2 Chung Seong Jong 2

1중원대학교
2전북대학교

Accredited

ABSTRACT

Fast and high-quality document clustering algorithms play an important role in providing data exploration by organizing large amounts of information into a small number of meaningful clusters. Many papers have shown that the hierarchical clustering method takes good-performance, but is limited because of its quadratic time complexity. In contrast, with a large number of variables, K-means has a time complexity that is linear in the number of documents, but is thought to produce inferior clusters. In this paper, Condor system using K-Means algorithm Compares with regular method that the initial centroids have been established in advance, our method performance has been improved a lot.

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