Integer programming formulations and efficient local search for relaxed correlation clustering

Eduardo Queiroga (), Anand Subramanian (), Rosa Figueiredo () and Yuri Frota ()
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Eduardo Queiroga: Universidade Federal Fluminense
Anand Subramanian: Universidade Federal da Paraíba
Rosa Figueiredo: Avignon Université
Yuri Frota: Universidade Federal Fluminense

Journal of Global Optimization, 2021, vol. 81, issue 4, No 5, 919-966

Abstract: Abstract Relaxed correlation clustering (RCC) is a vertex partitioning problem that aims at minimizing the so-called relaxed imbalance in signed graphs. RCC is considered to be an NP-hard unsupervised learning problem with applications in biology, economy, image recognition and social network analysis. In order to solve it, we propose two linear integer programming formulations and a local search-based metaheuristic. The latter relies on auxiliary data structures to efficiently perform move evaluations during the search process. Extensive computational experiments on existing and newly proposed benchmark instances demonstrate the superior performance of the proposed approaches when compared to those available in the literature. While the exact approaches obtained optimal solutions for open problems, the proposed heuristic algorithm was capable of finding high quality solutions within a reasonable CPU time. In addition, we also report improving results for the symmetrical version of the problem. Moreover, we show the benefits of implementing the efficient move evaluation procedure that enables the proposed metaheuristic to be scalable, even for large-size instances.

Keywords: Relaxed correlation clustering; Unsupervised learning; Integer programming; Iterated local search (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10898-020-00989-7

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