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Vol.21 No.4
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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 / Journal of the Korean Society for Information Management, (P)1013-0799; (E)2586-2073
2004, v.21 no.4, pp.173-185
https://doi.org/10.3743/KOSIM.2004.21.4.173



& (2004). Selection of Cluster Hierarchy Depth and Initial Centroids in Hierarchical Clustering using K-Means Algorithm. Journal of the Korean Society for Information Management, 21(4), 173-185, https://doi.org/10.3743/KOSIM.2004.21.4.173
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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.

keywords
문서 클러스터링, K-Means 알고리즘, 클러스터 계층 깊이, 클러스터 초기값, 계층적 클러스터링, 클러스터 중심document clustering, K-Means algorithm, cluster hierarchy depth, cluster initial value, hierarchical clustering, cluster centroid, 문서 클러스터링, K-Means 알고리즘, 클러스터 계층 깊이, 클러스터 초기값, 계층적 클러스터링, 클러스터 중심document clustering, K-Means algorithm, cluster hierarchy depth, cluster initial value, hierarchical clustering, cluster centroid

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