On Fuzzy c-Means for Data with Tolerance
On Fuzzy c-Means for Data with Tolerance
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DOI：
10.20965/jaciii.2006.p0673
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发表时间：
2006-04
期刊：
影响因子：
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通讯作者：
Ryuichi Murata;Y. Endo;Hideyuki Haruyama;S. Miyamoto
中科院分区：
文献类型：
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作者：
Ryuichi Murata;Y. Endo;Hideyuki Haruyama;S. Miyamoto
本文提出了两种新的基于熵正则化模糊c均值的聚类算法，它们可以处理具有一定误差的数据。首先，在涉及聚类的优化问题中引入公差，即误差的允许范围，并给出公差的公式。然后，利用库恩-塔克条件求解问题。最后，根据问题的求解结果构建算法。
This paper presents two new clustering algorithms which are based on the entropy regularized fuzzy c-means and can treat data with some errors. First, the tolerance which means the permissible range of the error is introduced into optimization problems which relate with clustering, and the tolerance is formulated. Next, the problems are solved using Kuhn-Tucker conditions. Last, the algorithms are constructed based on the results of solving the problems.