RK-Means Clustering: K-Means with Reliability

• HUA Chunsheng

  Faculty of System Engineering, Wakayama University Presently, Institute of Scientific and Industrial Research, Osaka University

• CHEN Qian

  Faculty of System Engineering, Wakayama University

• WU Haiyuan

  Faculty of System Engineering, Wakayama University The Institute of Electronics, Information and Communication Engineers

• WADA Toshikazu

  Faculty of System Engineering, Wakayama University The Institute of Electronics, Information and Communication Engineers

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.

Journal

• IEICE Transactions on Information and Systems

  IEICE Transactions on Information and Systems E91-D (1), 96-104, 2008

  The Institute of Electronics, Information and Communication Engineers

Keywords

• robust clustering
• reliability evaluation
• K-means clustering
• data classification

Details

• CRID: 1390001204377351168
• NII Article ID: 10026800730
• NII Book ID: AA10826272
• DOI: 10.1093/ietisy/e91-d.1.96
• ISSN: 17451361
• ISSN: 09168532
• Web Site: https://www.jstage.jst.go.jp/article/transinf/E91.D/1/E91.D_1_96/_pdf
• Text Lang: en
• Data Source:
  • JaLC
  • Crossref
  • CiNii Articles
• Abstract License Flag: Disallowed