Responsive threshold search based memetic algorithm for balanced minimum sum-of-squares clustering

•An efficient population based memetic algorithm is proposed for BMSSC.•A powerful responsive threshold search method is used for local optimization.•Memetic algorithm uses a backbone-based crossover for solution recombination.•The reported results are very competitive compared to existing best perf...

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Veröffentlicht in:Information sciences Jg. 569; S. 184 - 204
Hauptverfasser: Zhou, Qing, Hao, Jin-Kao, Wu, Qinghua
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Inc 01.08.2021
Elsevier
Schlagworte:
Balanced clustering
Computer Science
Heuristics
Memetic algorithm
Minimum sum-of-squares
Responsive threshold search
Minimum sum-of-squares
Memetic algorithm
Responsive threshold search
Heuristics
Balanced clustering
ISSN:0020-0255, 1872-6291
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Zusammenfassung:•An efficient population based memetic algorithm is proposed for BMSSC.•A powerful responsive threshold search method is used for local optimization.•Memetic algorithm uses a backbone-based crossover for solution recombination.•The reported results are very competitive compared to existing best performing heuristics.•The key essentials to the good performance of the algorithm are investigated. Clustering is a common task in data mining for constructing well-separated groups (clusters) from a large set of data points. The balanced minimum sum-of-squares clustering problem is a variant of the classic minimum sum-of-squares clustering (MSSC) problem and arises from broad real-life applications where the cardinalities of any two clusters differ by at most one. This study presents the first memetic algorithm for solving the balanced MSSC problem. The proposed algorithm combines a backbone-based crossover operator for generating offspring solutions and a responsive threshold search that alternates between a threshold-based exploration procedure and a descent-based improvement procedure for improving new offspring solutions. Numerical results on 16 real-life datasets show that the proposed algorithm competes very favorably with several state-of-the-art methods from the literature. Key components of the proposed algorithm are investigated to understand their effects on the performance of the algorithm.
ISSN:0020-0255
1872-6291
DOI:10.1016/j.ins.2021.04.014