- Title
- A heuristic algorithm for solving the minimum sum-of-squares clustering problems
- Creator
- Ordin, Burak; Bagirov, Adil
- Date
- 2015
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/76578
- Identifier
- vital:7580
- Identifier
-
https://doi.org/10.1007/s10898-014-0171-5
- Identifier
- ISSN:0925-5001
- Abstract
- Clustering is an important task in data mining. It can be formulated as a global optimization problem which is challenging for existing global optimization techniques even in medium size data sets. Various heuristics were developed to solve the clustering problem. The global k-means and modified global k-means are among most efficient heuristics for solving the minimum sum-of-squares clustering problem. However, these algorithms are not always accurate in finding global or near global solutions to the clustering problem. In this paper, we introduce a new algorithm to improve the accuracy of the modified global k-means algorithm in finding global solutions. We use an auxiliary cluster problem to generate a set of initial points and apply the k-means algorithm starting from these points to find the global solution to the clustering problems. Numerical results on 16 real-world data sets clearly demonstrate the superiority of the proposed algorithm over the global and modified global k-means algorithms in finding global solutions to clustering problems.
- Publisher
- Kluwer Academic Publishers
- Relation
- Journal of Global Optimization Vol. 61, no. 2 (2015), p. 341-361; http://purl.org/au-research/grants/arc/DP140103213
- Rights
- Copyright Kluwer
- Rights
- This metadata is freely available under a CCO license
- Subject
- Global k-means algorithm; Global optimization; k-means algorithm; Minimum sum-of-squares clustering; Nonsmooth optimization; Algorithms; Data mining; Heuristic algorithms; Optimization; Problem solving; Virtual reality; Algorithm for solving; Clustering problems; Global K-means algorithm; Global optimization problems; Global optimization techniques; k-Means algorithm; Clustering algorithms; 0102 Applied Mathematics; 0103 Numerical and Computational Mathematics; 0802 Computation Theory and Mathematics
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