A learning based self-organized additive fuzzy clustering method and its application for EEG data

Abstract

In this paper, a learning based fuzzy clustering method and its application to a set of electroencephalogram (EEG) data is given. The proposed method combines the learning process of noise to a conventional self-organized additive fuzzy clustering method. This is done by using the inner product of a pair of degrees of belongingness of objects. By learning the status of the noise in each iteration of the algorithm, the proposed method can obtain a more adaptable result.