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An Approach to Online Fuzzy Clustering Based on the Mahalanobis Distance Measure

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成果类型:
期刊论文、会议论文
作者:
Zhengbing Hu;Oleksii K. Tyshchenko
通讯作者:
Tyshchenko, O.K.
作者机构:
[Hu Z.] School of Educational Information Technology, Central China Normal University, 152 Louyu Road, Wuhan, 430079, China
[Tyshchenko O.K.] Institute for Research and Applications of Fuzzy Modeling, CE IT4Innovations, University of Ostrava, 30. dubna 22, Ostrava, 701 03, Czech Republic
通讯机构:
[Tyshchenko, O.K.] I
Institute for Research and Applications of Fuzzy Modeling, 30. dubna 22, Czech Republic
语种:
英文
关键词:
Computational intelligence;Distance measure;Fuzzifier;Fuzzy clustering;Membership function;Objective function
期刊:
Advances in Intelligent Systems and Computing
ISSN:
2194-5357
年:
2020
卷:
1127
页码:
364-374
会议名称:
International Symposium on Computer Science, Digital Economy and Intelligent Systems, CSDEIS 2019
会议论文集名称:
Advances in Intelligent Systems, Computer Science and Digital Economics
会议时间:
4 October 2019 through 6 October 2019
主编:
Hu Z.Petoukhov S.He M.
出版者:
Springer
ISBN:
9783030392154
基金类别:
The investigation of Oleksii K. Tyshchenko was also granted by the National Science Agency of the Czech Republic within the project TACR TL01000351.
机构署名:
本校为第一机构
院系归属:
教育信息技术学院
摘要:
The manuscript gives consideration to the problem of fuzzy clustering data streams. The offered approach incorporates the concepts of the probabilistic fuzzy clustering based on the specific sort of distance metrics. The main emphasis of the study was put on the application of Mahalanobis measures in the fuzzy clustering algorithms that let design classes of a hyperellipsoidal shape which can change the orientation of their axes in a feature space. The substantial hallmark of the presented fuzzy clustering scheme is its aptitude to group data i...

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