UM
The Construction of Cascaded Features and Its Application in Fuzzy Clustering
Zhao,Yin Ping; Chen,Long; Philip Chen,C. L.
2018-12-01
Source Publication2018 International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2018
Pages294-298
AbstractThe success of fuzzy clustering heavily relies on the proper feature space constructed by the input data. For nonspherical and overlapped clusters, kernel fuzzy clustering is more effective owing to it finds more proper feature space compared to conventional fuzzy clustering. Unfortunately, poor scalability of kernel fuzzy clustering is induced by the requirement of large memory and running time. To solve the problem, random feature based method was presented to approximate the kernel function. Features in this approximate feature space are very useful information. Inspired by the architecture of functional-link neural network, to represent the diversity of features, cascaded features are constructed by a new feature mapping technique called cascaded feature mapping in this paper. By performing classical fuzzy c-means (FCM) with the cascaded features, a new fuzzy clustering algorithm called FCM-CF is developed. The experimental results of our proposed methods verify the superiority in comparison of other classical fuzzy clustering methods.
KeywordCascaded Feature Fuzzy Clustering Kernel Random Feature
DOI10.1109/SPAC46244.2018.8965479
URLView the original
Language英語English
Scopus ID2-s2.0-85079166446
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Document TypeConference paper
CollectionUniversity of Macau
AffiliationUniversity of Macau,Faculty of Science and Technology,Macao
First Author AffilicationFaculty of Science and Technology
Recommended Citation
GB/T 7714
Zhao,Yin Ping,Chen,Long,Philip Chen,C. L.. The Construction of Cascaded Features and Its Application in Fuzzy Clustering[C],2018:294-298.
APA Zhao,Yin Ping,Chen,Long,&Philip Chen,C. L..(2018).The Construction of Cascaded Features and Its Application in Fuzzy Clustering.2018 International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2018,294-298.
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