Data Stream Online Clustering Based on Fuzzy Expectation-Maximization Approach

被引：0
|
作者：
Deineko, Anastasiia O. [1 ]
Zhernova, Polina Ye [2 ]
Gordon, Boris [3 ]
Zayika, Oleksandr O. [1 ]
Pliss, Iryna [4 ]
Pabyrivska, Nelya [5 ]
机构：
[1] Kharkiv Natl Univ Radio Elect, Artificial Intelligence Dept, Kharkov, Ukraine
[2] Kharkiv Natl Univ Radio Elect, Syst Engn Dept, Kharkov, Ukraine
[3] Tallinn Univ Technol, Comp Syst Dept, Tallinn, Estonia
[4] Kharkiv Natl Univ Radio Elect, Control Syst Res Lab, Kharkov, Ukraine
[5] Lviv Polytech Natl Univ, Dept Math, Lvov, Ukraine
来源：
2018 IEEE SECOND INTERNATIONAL CONFERENCE ON DATA STREAM MINING & PROCESSING (DSMP) | 2018年
关键词：
big data; dynamic data mining; data stream mining; computational intelligence; EM-algorithm; fuzzy clustering; Kohonen's self-learning; soft clustering;
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：
In the paper the online fuzzy clustering recurrent procedure has been introduced that allows the forming of hyperellipsoidal clusters with an arbitrary orientation of the axes is proposed. Such clustering system is the generalization of a number of known algorithms, it is intended to solve tasks within the general problems of Data Stream Mining (DSM) and Dynamic Data Mining (DDM), when information is sequentially fed to processing in online mode.
引用
收藏
页码：171 / 176
页数：6
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