- Title
- Nonsmooth optimization-based model and algorithm for semisupervised clustering
- Creator
- Bagirov, Adil; Taheri, Sona; Bai, Fusheng; Zheng, Fangying
- Date
- 2023
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/197996
- Identifier
- vital:18965
- Identifier
-
https://doi.org/10.1109/TNNLS.2021.3129370
- Identifier
- ISSN:2162-237X (ISSN)
- Abstract
- Using a nonconvex nonsmooth optimization approach, we introduce a model for semisupervised clustering (SSC) with pairwise constraints. In this model, the objective function is represented as a sum of three terms: the first term reflects the clustering error for unlabeled data points, the second term expresses the error for data points with must-link (ML) constraints, and the third term represents the error for data points with cannot-link (CL) constraints. This function is nonconvex and nonsmooth. To find its optimal solutions, we introduce an adaptive SSC (A-SSC) algorithm. This algorithm is based on the combination of the nonsmooth optimization method and an incremental approach, which involves the auxiliary SSC problem. The algorithm constructs clusters incrementally starting from one cluster and gradually adding one cluster center at each iteration. The solutions to the auxiliary SSC problem are utilized as starting points for solving the nonconvex SSC problem. The discrete gradient method (DGM) of nonsmooth optimization is applied to solve the underlying nonsmooth optimization problems. This method does not require subgradient evaluations and uses only function values. The performance of the A-SSC algorithm is evaluated and compared with four benchmarking SSC algorithms on one synthetic and 12 real-world datasets. Results demonstrate that the proposed algorithm outperforms the other four algorithms in identifying compact and well-separated clusters while satisfying most constraints. © 2021 IEEE.
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Relation
- IEEE Transactions on Neural Networks and Learning Systems Vol. 34, no. 9 (2023), p. 5517-5530; http://purl.org/au-research/grants/arc/DP190100580
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright © 2021 IEEE
- Subject
- 4602 Artificial intelligence; Cluster analysis; Discrete gradient method (DGM); Nonsmooth optimization; Pairwise constraints; Semisupervised clustering (SSC)
- Reviewed
- Funder
- The work of Adil M. Bagirov and Sona Taheri was supported by the Australian Government through the Australian Research Council's Discovery Projects funding scheme under Project DP190100580. The work of Fusheng Bai was supported in part by the National Natural Science Foundation of China under Project 11871128 and Project 11991024 and in part by the Chongqing Natural Science Foundation, China, under Project cstc2019jcyj-msxmX0368. The work of Fangying Zheng was supported by the Natural Science Foundation of Zhejiang Province, China, under Project LY19A010025
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