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

Authors:
Tomoyuki Kuwata;Mika Sato-Ilic;Lakhmi C. Jain
Affiliations:
Faculty of Systems and Information Engineering, University of Tsukuba, Tsukuba, Ibaraki, Japan;Faculty of Systems and Information Engineering, University of Tsukuba, Tsukuba, Ibaraki, Japan;School of Electrical and Information Engineering, University of South Australia, Adelaide, South Australia, SA, Australia
Venue:
International Journal of Knowledge-based and Intelligent Engineering Systems - Intelligent Information Processing: Techniques and Applications
Year:
2012

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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.