An evolving connectionist system for data stream fuzzy clustering and its online learning

被引：12
|
作者：
Bodyanskiy, Yevgeniy V. [1 ]
Tyshchenko, Oleksii K. [1 ]
Kopaliani, Daria S. [1 ]
机构：
[1] Kharkiv Natl Univ Radio Elect, Control Syst Res Lab, 14 Nauky Ave, UA-61166 Kharkov, Ukraine
来源：
NEUROCOMPUTING | 2017年 / 262卷
关键词：
Evolving connectionist system; Neuro-fuzzy network; Data stream; Fuzzy clustering; C-MEANS; INFERENCE SYSTEM; NEURAL-NETWORK; IDENTIFICATION; CLASSIFIER; FLEXFIS; ETS;
D O I：
10.1016/j.neucom.2017.03.081
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
An evolving cascade neuro-fuzzy system and its online learning procedure are considered in this paper. The system is based on conventional Kohonen neurons. The proposed system solves a clustering task of non-stationary data streams under uncertainty conditions when data come in the form of a sequential stream in an online mode. A quality estimation process is defined by finding an optimal value of the used cluster validity index. (C) 2017 Elsevier B.V. All rights reserved.
引用
收藏
页码：41 / 56
页数：16
相关论文
共 50 条
  • [31] A fuzzy approach for interpretation of ubiquitous data stream clustering and its application in road safety
    Horovitz, Osnat
    Krishnaswamy, Shonali
    Gaber, Mohamed Medhat
    [J]. INTELLIGENT DATA ANALYSIS, 2007, 11 (01) : 89 - 108
  • [32] Online Clustering for Trajectory Data Stream of Moving Objects
    Yu, Yanwei
    Wang, Qin
    Wang, Xiaodong
    Wang, Huan
    He, Jie
    [J]. COMPUTER SCIENCE AND INFORMATION SYSTEMS, 2013, 10 (03) : 1293 - 1317
  • [33] Online System Prognostics with Ensemble Models and Evolving Clustering
    Tseng, Fling
    Filev, Dimitar
    Yildirim, Murat
    Chinnam, Ratna Babu
    [J]. MACHINES, 2023, 11 (01)
  • [34] An Ensemble Learning Approach for Data Stream Clustering
    Fathzadeh, Ramin
    Mokhtari, Vahid
    [J]. 2013 21ST IRANIAN CONFERENCE ON ELECTRICAL ENGINEERING (ICEE), 2013,
  • [35] Online Evolving Fuzzy Rule-Based Prediction Model For High Frequency Trading Financial Data Stream
    Gu, Xiaowei
    Angelov, Plamen P.
    Ali, Azliza Mohd
    Gruver, William A.
    Gaydadjiev, Georgi
    [J]. PROCEEDINGS OF THE 2016 IEEE CONFERENCE ON EVOLVING AND ADAPTIVE INTELLIGENT SYSTEMS (EAIS), 2016, : 169 - 175
  • [36] An overview on evolving systems and learning from stream data
    Daniel Leite
    Igor Škrjanc
    Fernando Gomide
    [J]. Evolving Systems, 2020, 11 : 181 - 198
  • [37] An overview on evolving systems and learning from stream data
    Leite, Daniel
    Skrjanc, Igor
    Gomide, Fernando
    [J]. EVOLVING SYSTEMS, 2020, 11 (02) : 181 - 198
  • [38] Clustering Based on Correlation Fractal Dimension Over an Evolving Data Stream
    Yarlagadda, Anuradha
    Jonnalagedda, Murthy
    Munaga, Krishna
    [J]. INTERNATIONAL ARAB JOURNAL OF INFORMATION TECHNOLOGY, 2018, 15 (01) : 1 - 9
  • [39] Density-Based Clustering over an Evolving Data Stream with Noise
    Cao, Feng
    Ester, Martin
    Qian, Weining
    Zhou, Aoying
    [J]. PROCEEDINGS OF THE SIXTH SIAM INTERNATIONAL CONFERENCE ON DATA MINING, 2006, : 328 - +
  • [40] Evolving clustering algorithm based on mixture of typicalities for stream data mining
    Maia, Jose
    Severiano Junior, Carlos Alberto
    Guimaraes, Frederico Gadelha
    de Castro, Cristiano Leite
    Lemos, Andre Paim
    Fonseca Galindo, Juan Camilo
    Cohen, Miri Weiss
    [J]. FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 2020, 106 : 672 - 684
12345