Nonlinear Data Clustering by Competitive Learning Model and Application to Document Information

Abstract

Clustering by competitive learning is one of the most popular techniques in cluster analysis. However, it is well-known that this method cannot produce nonlinear cluster boundaries. To obtain nonlinear cluster boundaries, the use of the kernel method can be considered. This paper aims at expanding the competitive learning clustering to nonlinear clustering algorithm to handle nonlinear data set by using the kernel method. Moreover document information is modeled by using fuzzy multisets and the effectiveness of the proposed method is shown in numerical examples.