期刊
EXPERT SYSTEMS WITH APPLICATIONS
卷 36, 期 2, 页码 3336-3341出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2008.01.039
关键词
Clustering; K-means; K-medoids; Rand index
类别
资金
- KOSEF through System Bio-Dynamics research center at POSTECH
This paper proposes a new algorithm for K-medoids clustering which runs like the K-means algorithm and tests several methods for selecting initial medoids. The proposed algorithm calculates the distance matrix once and uses it for finding new medoids at every iterative step. To evaluate the proposed algorithm, we use some real and artificial data sets and compare with the results of other algorithms in terms of the adjusted Rand index. Experimental results show that the proposed algorithm takes a significantly reduced time ill computation with comparable performance against the partitioning around medoids. (C) 2008 Elsevier Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据