On Various Types of Even-Sized Clustering Based on Optimization
Clustering is a very useful tool of data mining. A clustering method which is referred to as K-member clustering is to classify a dataset into some clusters of which the size is more than a given constant K. The K-member clustering is useful and it is applied to many applications. Naturally, clustering methods to classify a dataset into some even-sized clusters can be considered and some even-sized clustering methods have been proposed. However, conventional even-sized clustering methods often output inadequate results. One of the reasons is that they are not based on optimization. Therefore, we proposed Even-sized Clustering Based on Optimization (ECBO) in our previous study. The simplex method is used to calculate the belongingness of each object to clusters in ECBO. In this study, ECBO is extended by introducing some ideas which were introduced in k-means or fuzzy c-means to improve problems of initial-value dependence, robustness against outliers, calculation cost, and nonlinear boundaries of clusters. Moreover, we reconsider the relation between the dataset size, the cluster number, and K in ECBO.
We would like to thank gratefully and sincerely Professor Emeritus Sadaaki Miyamoto of University of Tsukuba, Japan, Professor Vicenç Torra of University of Skövde, Sweden, and Associate Professor Yuchi Kanzawa of Shibaura Institute of Technology, Japan, for their advice. This study was supported by JSPS KAKENHI Grant Numbers JP26330270, JP26330271, and JP16K16128.
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