UM
Random Maclaurin Feature-based Fuzzy Clustering
Wang,Yingxu; Chen,Long; Li,Tianjun; Tian,Yifei
2020-11-13
Source Publication2020 7th International Conference on Information, Cybernetics, and Computational Social Systems, ICCSS 2020
Pages417-422
AbstractTraditional kernel-based fuzzy clustering helps to discover the complex structures hidden in data, but it suffers from high computational complexity. This paper introduces random Maclaurin feature (RMF) into fuzzy clustering, and proposes a random Maclaurin feature-based fuzzy clustering algorithm (RMFCM). In this method, the RMF is used to approximate polynomial kernel, and the fuzzy clustering with linear computational complexity is conducted in generated feature space. The experiments carried out on four synthetic datasets and four UCI real-world datasets prove the effectiveness and efficiency of the proposed RMFCM method. The influence of the dimension of the RMF is also studied in the experiments.
Keywordfuzzy clustering kernel-based clustering polynomial kernel random Maclaurin feature
DOI10.1109/ICCSS52145.2020.9336879
URLView the original
Language英语
Scopus ID2-s2.0-85100895574
Fulltext Access
Citation statistics
Document TypeConference paper
CollectionUniversity of Macau
Corresponding AuthorChen,Long
AffiliationUniversity of Macau,Department of Computer and Information Science,Macau,Macao
First Author AffilicationUniversity of Macau
Corresponding Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Wang,Yingxu,Chen,Long,Li,Tianjun,et al. Random Maclaurin Feature-based Fuzzy Clustering[C],2020:417-422.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Wang,Yingxu]'s Articles
[Chen,Long]'s Articles
[Li,Tianjun]'s Articles
Baidu academic
Similar articles in Baidu academic
[Wang,Yingxu]'s Articles
[Chen,Long]'s Articles
[Li,Tianjun]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Wang,Yingxu]'s Articles
[Chen,Long]'s Articles
[Li,Tianjun]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.