- Title
- A novel optimization approach towards improving separability of clusters
- Creator
- Bagirov, Adil; Hoseini-Monjezi, Najmeh; Taheri, Sona
- Date
- 2023
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/193574
- Identifier
- vital:18191
- Identifier
-
https://doi.org/10.1016/j.cor.2022.106135
- Identifier
- ISSN:0305-0548 (ISSN)
- Abstract
- The objective functions in optimization models of the sum-of-squares clustering problem reflect intra-cluster similarity and inter-cluster dissimilarities and in general, optimal values of these functions can be considered as appropriate measures for compactness of clusters. However, the use of the objective function alone may not lead to the finding of separable clusters. To address this shortcoming in existing models for clustering, we develop a new optimization model where the objective function is represented as a sum of two terms reflecting the compactness and separability of clusters. Based on this model we develop a two-phase incremental clustering algorithm. In the first phase, the clustering function is minimized to find compact clusters and in the second phase, a new model is applied to improve the separability of clusters. The Davies–Bouldin cluster validity index is applied as an additional measure to compare the compactness of clusters and silhouette coefficients are used to estimate the separability of clusters. The performance of the proposed algorithm is demonstrated and compared with that of four other algorithms using synthetic and real-world data sets. Numerical results clearly show that in comparison with other algorithms the new algorithm is able to find clusters with better separability and similar compactness. © 2022
- Publisher
- Elsevier Ltd
- Relation
- Computers and Operations Research Vol. 152, no. (2023), p.; 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
- Crown Copyright @ 2022
- Subject
- 3509 Transportation, logistics and supply chains; 4901 Applied mathematics; Cluster analysis; Cluster validity indices; Incremental clustering; Nonsmooth optimization
- Reviewed
- Funder
- The research by A.M. Bagirov is supported by the Australian Government under Australian Research Council ’s Discovery Projects funding scheme [Project No. DP190100580 ] and the research by N. Hoseini Monjezi is supported by Iran National Elite Foundation and Iran National Science Foundation [INSF, No. 99013933 ].
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