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A tabu search algorithm for cohesive clustering problems

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Abstract

Clustering problems can be found in a wide range of applications including data mining/analytics, logistics, healthcare, biotechnology, economic analysis and many other areas. Solving a clustering problem from the real world often poses significant challenges in spite of the fact that extensive research has been devoted to this topic. In this paper we present a tabu Search algorithm for a new problem class called cohesive clustering which arises in a variety of business applications. The class introduces an objective function to produce clusters as “pure” as possible, to maximize the similarity of the elements in each given cluster. Tabu search intensification and diversification strategies are employed in order to produce enhanced outcomes. The computational results demonstrate the effectiveness of the proposed algorithm.

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Acknowledgments

The authors would like to thank our student team including Aro Lee, Zheng Xu, and Jiayao Gao for their efforts in implementing the algorithm, data preparations, and partial computational experiments. We would also like to express our gratitude to two anonymous referees for their valuable criticisms and suggestions to improve our manuscript. This research is partially supported by project contract CIUC20140004.

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Correspondence to Buyang Cao.

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Cao, B., Glover, F. & Rego, C. A tabu search algorithm for cohesive clustering problems. J Heuristics 21, 457–477 (2015). https://doi.org/10.1007/s10732-015-9285-2

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  • DOI: https://doi.org/10.1007/s10732-015-9285-2

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