A Note on Even-Sized Clustering Based on Optimization
A Note on Even-Sized Clustering Based on Optimization
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DOI：
10.1109/scis-isis.2016.0092
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发表时间：
2016-08
期刊：
影响因子：
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通讯作者：
Tsubasa Hirano;Y. Endo;Naohiko Kinoshita;S. Miyamoto
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文献类型：
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作者：
Tsubasa Hirano;Y. Endo;Naohiko Kinoshita;S. Miyamoto
到目前为止，已经提出了一些聚类方法来将数据集分类为一些簇的大小大于常数K。这些方法被称为K-成员聚类，在许多应用中非常有用。考虑聚类方法来将数据集分类为均匀大小的聚类是很自然的，实际上，也已经提出了这样的方法。然而，它们往往产生不充分的结果。据认为，原因是它们不是基于优化。因此，在先前的研究中，我们提出了基于优化的均匀大小聚类（ECBO）。利用单纯形法计算隶属度，改进了聚类结果。在本研究中，我们借由引入中心点与核心的概念，提出几种扩充的ECBO。
Some clustering methods to classify a dataset into some clusters of which the size is more than a constant K have been proposed until now. The methods are called K-member clustering and very useful for many applications. It is natural to consider clustering methods to classify a dataset into even-sized clusters, and actually, such methods have been also proposed. However, they often output inadequate results. It is considered that the reason is that they are not based on optimization. Therefore, in the previous study, we proposed Even-sized Clustering Based on Optimization (ECBO). We improved the clustering results by the simplex method to calculate the membership grade. In this study, we propose some types of extended ECBO by introducing some concept of medoid and kernel to improve ECBO.