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Deep Fuzzy Clustering-A Representation Learning Approach
Feng,Qiying1; Chen,Long1; Philip Chen,C. L.1,2,3; Guo,Li3
Source PublicationIEEE Transactions on Fuzzy Systems

Fuzzy clustering is a classical approach to provide the soft partition of data. Although its enhancements have been intensively explored, fuzzy clustering still suffers from the difficulties in handling real high-dimensional data with complex latent distribution. To solve the problem, this article proposes a deep fuzzy clustering method by representing the data in a feature space produced by the deep neural network. From the perspective of representation learning, three constraints or objectives are imposed to the neural network to enhance the clustering-friendly representation. At first, as a good representation of data, the mapped data in the new feature space should support the reconstruction of original data. So, the autoencoder architecture is applied to ensure that the original data can be recovered by decoding the encoded representation with another neural network. Second, to solve the clustering problem efficiently, the intracluster compactness and the intercluster separability are to be minimized and maximized, respectively, in the new feature space. At last, considering that the data in the same class should be close to each other, the affinities between new representations are tuned in accordance with the discriminative information. Altogether, we design a graph-regularized deep normalized fuzzy compactness and separation clustering model to conduct representation learning and soft clustering simultaneously. The learning algorithm based on stochastic gradient descent is proposed to the model, and the comparative studies with baseline clustering algorithms on real-world data illustrate the superiority of the proposal.

KeywordDeep Learning Discriminative Graph Fuzzy C-means (Fcm) Fuzzy Compactness And Separation (Fcs) Pseudolabel
URLView the original
Indexed BySCIE
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000545205300020
Scopus ID2-s2.0-85087858366
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Cited Times [WOS]:17   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
Corresponding AuthorChen,Long
Affiliation1.University of Macau
2.South China University of Technology
3.Dalian Maritime University
4.Qingdao University
First Author AffilicationUniversity of Macau
Corresponding Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Feng,Qiying,Chen,Long,Philip Chen,C. L.,et al. Deep Fuzzy Clustering-A Representation Learning Approach[J]. IEEE Transactions on Fuzzy Systems,2020,28(7):1420-1433.
APA Feng,Qiying,Chen,Long,Philip Chen,C. L.,&Guo,Li.(2020).Deep Fuzzy Clustering-A Representation Learning Approach.IEEE Transactions on Fuzzy Systems,28(7),1420-1433.
MLA Feng,Qiying,et al."Deep Fuzzy Clustering-A Representation Learning Approach".IEEE Transactions on Fuzzy Systems 28.7(2020):1420-1433.
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