- Title
- An incremental nonsmooth optimization algorithm for clustering using L1 and L∞ norms
- Creator
- Ordin, Burak; Bagirov, Adil; Mohebi, Ehsam
- Date
- 2020
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/175170
- Identifier
- vital:14936
- Identifier
-
https://doi.org/10.3934/jimo.2019079
- Identifier
- ISBN:1547-5816 (ISSN)
- Abstract
- An algorithm is developed for solving clustering problems with the similarity measure defined using the L1and L∞ norms. It is based on an incremental approach and applies nonsmooth optimization methods to find cluster centers. Computational results on 12 data sets are reported and the proposed algorithm is compared with the X-means algorithm. ©
- Publisher
- American Institute of Mathematical Sciences
- Relation
- Journal of Industrial and Management Optimization Vol. 16, no. 6 (2020), p. 2757-2779; 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 American Institute of Mathematical Sciences
- Subject
- Cluster analysis; Incremental algorithm; Nonconvex optimization; Nonsmooth optimization; Unsupervised learning; 0102 Applied Mathematics; 0103 Numerical and Computational Mathematics
- Reviewed
- Funder
- Australian Research Council’s Discovery Projects scheme (Project No.: DP190100580).
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