- Title
- Clustering in large data sets with the limited memory bundle method
- Creator
- Karmitsa, Napsu; Bagirov, Adil; Taheri, Sona
- Date
- 2018
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/165541
- Identifier
- vital:13303
- Identifier
-
https://doi.org/10.1016/j.patcog.2018.05.028
- Identifier
- ISBN:0031-3203
- Abstract
- The aim of this paper is to design an algorithm based on nonsmooth optimization techniques to solve the minimum sum-of-squares clustering problems in very large data sets. First, the clustering problem is formulated as a nonsmooth optimization problem. Then the limited memory bundle method [Haarala et al., 2007] is modified and combined with an incremental approach to design a new clustering algorithm. The algorithm is evaluated using real world data sets with both the large number of attributes and the large number of data points. It is also compared with some other optimization based clustering algorithms. The numerical results demonstrate the efficiency of the proposed algorithm for clustering in very large data sets.
- Publisher
- Elsevier Ltd
- Relation
- Pattern Recognition Vol. 83, no. (2018), p. 245-259; http://purl.org/au-research/grants/arc/DP140103213
- Rights
- Copyright © 2018 Elsevier Ltd
- Rights
- This metadata is freely available under a CCO license
- Subject
- 0801 Artificial Intelligence and Image Processing; 0806 Information Systems; 0906 Electrical and Electronic Engineering; Bundle methods; Cluster analysis; Limited memory methods; Nonconvex optimization; Nonsmooth optimization
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