Random Maclaurin Feature-based Fuzzy Clustering
Wang,Yingxu; Chen,Long; Li,Tianjun; Tian,Yifei
Source Publication2020 7th International Conference on Information, Cybernetics, and Computational Social Systems, ICCSS 2020
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
URLView the original
Scopus ID2-s2.0-85100895574
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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.
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