On Sequential Cluster Extraction Using Possibilistic Size Controll Clustering

Abstract

Clustering methods such as k-means and fuzzy c-means methods require the number of clusters to be determined in advance. In addition, conventional clustering methods may not be able to properly classify unbalanced data or data containing outliers. In this study, we propose a sequential cluster extraction method based on controlled-sized sequential possibilisitic clustering for imbalanced data and data with outliers. Furthermore, numerical experiments were conducted to confirm the effectiveness of the proposed method.