On even-sized clustering algorithm based on optimization
Tsubasa Hirano, Yasunori Endo, Naohiko Kinoshita, Yukihiro Hamasuna · 2014
Clustering methods to divide a data set into some clusters of which the size is more than a given constant K, are very useful in many applications. The methods are called K-member clustering (KMC). As a natural result, clustering methods to divide a data set into even-sized clusters can be considered. However, there are no algorithms of such methods based on optimization. That is why the conventional algorithms often output inadequate results. Therefore we should consider an algorithm based on optimization. In this paper, we propose evensized clustering algorithm using simplex method which is one of optimization method, and verify the proposed method through some numerical examples.