[1]BAO Guoqiang,YING Wenhao,JIANG Yizhang,et al.Fuzzy C-means clustering algorithm for multilayered hierarchical fusion fuzzy feature mapping[J].CAAI Transactions on Intelligent Systems,2018,13(4):594-601.[doi:10.11992/tis.201703047]
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Fuzzy C-means clustering algorithm for multilayered hierarchical fusion fuzzy feature mapping

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 13 Number of periods: 2018 4 Page number: 594-601 Column: 学术论文—机器学习 Public date: 2018-07-05
Title:
Fuzzy C-means clustering algorithm for multilayered hierarchical fusion fuzzy feature mapping
Author(s):
BAO Guoqiang1; 2;  YING Wenhao3;  JIANG Yizhang1; 2;  ZHANG Ying1; 2;  WANG Jun1; 2;  WANG Shitong1; 2
1. School of Digital Media, Jiangnan University, Wuxi 214122, China;
2. Jiangsu Key Laboratory of Media Design and Software Technology, Wuxi 214122, China;
3. School of Computer Science and Engineering, Changshu Institute of Technology, Changshu 215500, China
Keywords:
Takagi-Sugeno-Kang (TSK) fuzzy system; principal component analysis (PCA); unsupervised learning; fuzzy C-means clustering
CLC:
TP181
DOI:
10.11992/tis.201703047
Abstract:
In this paper, we propose a novel feature mapping technique called multilayer hierarchical fusion fuzzy feature mapping for the unsupervised learning of complex nonlinear data and combine it with the classical fuzzy C-means clustering. Based on the regular antecedent learning of the Takagi-Sugeno-Kang (TSK) fuzzy system, we first propose a novel fuzzy feature mapping method. Then, to address big data dimensions by fuzzy feature mapping, we propose a fuzzy feature mapping mechanism based on multilayer hierarchical fusion. This mechanism combines fuzzy feature mapping with principal component analysis (PCA), thereby avoiding the data confusion and redundancy caused by the high dimensionality of single-layer fuzzy feature mapping. Finally, we develop a novel FCM clustering algorithm based on multilayered hierarchical fusion feature mapping. The experimental results show that, in comparison with classical fuzzy clustering methods, the performance of the proposed algorithm is superior and more stable.
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