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

被引：2

作者：

Kuwata, Tomoyuki ^{[1
]}

Sato-Ilic, Mika

Jain, Lakhmi C. ^{[2
]}

机构：

[1] Univ Tsukuba, Fac Syst & Informat Engn, Tsukuba, Ibaraki, Japan

[2] Univ South Australia, Sch Elect & Informat Engn, Adelaide, SA, Australia

来源：

INTERNATIONAL JOURNAL OF KNOWLEDGE-BASED AND INTELLIGENT ENGINEERING SYSTEMS | 2012年 / 16卷 / 01期

关键词：

Fuzzy clustering; learning noise; self-organized similarity; electroencephalogram (EEG) data;

D O I：

10.3233/KES-2012-0238

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：69 / 78

页数：10

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