Fuzzy Clustering of Distorted Observations Based On Optimal Expansion Using Partial Distances

被引：0
|
作者：
Shafronenko, Alina [1 ]
Bodyanskiy, Yevgeniy [2 ]
Dolotov, Artem [2 ]
Setlak, Galina [3 ]
机构：
[1] Kharkiv Natl Univ Radio Elect, Informat Dept, Kharkov, Ukraine
[2] Kharkiv Natl Univ Radio Elect, Control Syst Res Lab, Kharkov, Ukraine
[3] Rzeszow Univ Technol, Rzeszow, Poland
来源：
2018 IEEE SECOND INTERNATIONAL CONFERENCE ON DATA STREAM MINING & PROCESSING (DSMP) | 2018年
关键词：
Kohonen self-organizing network; fuzzy clustering; incomplete observations with gaps; partial distance; optimal expansion;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The neural system that solves a problem of fuzzy clustering of distorted observations based on optimal expansion strategy using partial distance is proposed in this article. To solve this problem we propose the learning algorithm based on hybrid of rule "Winner-Takes-More" using modified self-organizing neuro-fuzzy Kohonen network. This modified system is characterized by basic characteristics, such as: high speed, simple numerical realization, processing of distorted information in online mode.
引用
收藏
页码：327 / 330
页数：4
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