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IDA
2010
Springer

Clustering with feature order preferences

9 years 1 months ago
Clustering with feature order preferences
We propose a clustering algorithm that effectively utilizes feature order preferences, which have the form that feature s is more important than feature t. Our clustering formulation aims to incorporate feature order preferences into prototype-based clustering. The derived algorithm automatically learns distortion measures parameterized by feature weights which will respect the feature order preferences as much as possible. Our method allows the use of a broad range of distortion measures such as Bregman divergences. Moreover, even when generalized entropy is used in the regularization term, the subproblem of learning the feature weights is still a convex programming problem. Empirical results on some datasets demonstrate the effectiveness and potential of our method.
Jun Sun, Wenbo Zhao, Jiangwei Xue, Zhiyong Shen, Y
Added 26 Jan 2011
Updated 26 Jan 2011
Type Journal
Year 2010
Where IDA
Authors Jun Sun, Wenbo Zhao, Jiangwei Xue, Zhiyong Shen, Yi-Dong Shen
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