Clustering with feature order preferences
Clustering with feature order preferences
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DOI：
10.3233/ida-2010-0433
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发表时间：
2010-12
影响因子：
9
通讯作者：
Junyi Sun;Wenbo Zhao;Jiangwei Xue;Zhiyong Shen;Yi-Dong Shen
中科院分区：
文献类型：
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作者：
Junyi Sun;Wenbo Zhao;Jiangwei Xue;Zhiyong Shen;Yi-Dong Shen
我们提出了一种有效利用特征顺序偏好的聚类算法，其形式为特征 s 比特征 t 更重要。我们的聚类公式旨在将特征顺序偏好纳入基于原型的聚类中。导出的算法自动学习由特征权重参数化的失真测量，这将尽可能尊重特征顺序偏好。我们的方法允许使用广泛的失真度量，例如布雷格曼散度。此外，即使在正则化项中使用广义熵，学习特征权重的子问题仍然是凸规划问题。一些数据集的实证结果证明了我们方法的有效性和潜力。
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.