Linear Fuzzy Clustering With Selection of Variables Using Graded Possibilistic Approach

Abstract

Linear fuzzy clustering is a useful tool for knowledge discovery in databases (KDD), and several modifications have been proposed in order to analyze real world data. This paper proposes a new approach for estimating local linear models, in which linear fuzzy clustering is performed by selecting variables that are useful for extracting correlation structure in each cluster. The new clustering model uses two types of memberships. One is the conventional membership that represents the degree of membership of each sample in each cluster. The other is the additional parameter that represents the relative responsibility of each variable for estimation of local linear models. The additional membership takes large values when the variable has close relationship with local principal components, and is calculated by using the graded possibilistic approach. Numerical experiments demonstrate that the proposed method is useful for identifying local linear model taking typicality of each variable into account.