IEICE Electronics Express
Online ISSN : 1349-2543
LETTER
K-maximin clustering: a maximin correlation approach to partition-based clustering
Taehoon LeeSeung Jean KimEui-Young ChungSungroh Yoon
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JOURNALS FREE ACCESS

Volume 6 (2009) Issue 17 Pages 1205-1211

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Abstract

We propose a new clustering algorithm based upon the maximin correlation analysis (MCA), a learning technique that can minimize the maximum misclassification risk. The proposed algorithm resembles conventional partition clustering algorithms such as k-means in that data objects are partitioned into k disjoint partitions. On the other hand, the proposed approach is unique in that an MCA-based approach is used to decide the location of the representative point for each partition. We test the proposed technique with typography data and show our approach outperforms the popular k-means and k-medoids clustering in terms of retrieving the inherent cluster membership.

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© 2009 by The Institute of Electronics, Information and Communication Engineers
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