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IEICE Transactions on Information and Systems 2008 E91-D(1):96-104; doi:10.1093/ietisy/e91-d.1.96
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Copyright © 2008 The Institute of Electronics, Information and Communication Engineers

Regular Section -- Papers -- Image Recognition, Computer Vision

RK-Means Clustering: K-Means with Reliability

Chunsheng HUA1,2, Qian CHEN1, Haiyuan WU1 and Toshikazu WADA1

1 The authors are with the Faculty of System Engineering, Wakayama University, Wakayama-shi, 640–8510 Japan. E-mail: wuhy{at}sys.wakayama-u.ac.jp, 2 Presently, with the Institute of Scientific and Industrial Research, Osaka University.


  Abstract

This paper presents an RK-means clustering algorithm which is developed for reliable data grouping by introducing a new reliability evaluation to the K-means clustering algorithm. The conventional K-means clustering algorithm has two shortfalls: 1) the clustering result will become unreliable if the assumed number of the clusters is incorrect; 2) during the update of a cluster center, all the data points belong to that cluster are used equally without considering how distant they are to the cluster center. In this paper, we introduce a new reliability evaluation to K-means clustering algorithm by considering the triangular relationship among each data point and its two nearest cluster centers. We applied the proposed algorithm to track objects in video sequence and confirmed its effectiveness and advantages.

Key Words: robust clustering, reliability evaluation, K-means clustering, data classification

Manuscript received May 2, 2006. Manuscript revised December 5, 2006.


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