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
收藏
页码：69 / 78
页数：10
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