Population-based bio-inspired algorithms for cluster ensembles optimization


Clustering algorithms have been applied to different problems in many different real-word applications. Nevertheless, each algorithm has its own advantages and drawbacks, which can result in different solutions for the same problem. Therefore, the combination of different clustering algorithms (cluster ensembles) has emerged as an attempt to overcome the limitations of each clustering technique. The use of cluster ensembles aims to combine multiple partitions generated by different clustering algorithms into a single clustering solution (consensus partition). Recently, several approaches have been proposed in the literature in order to optimize or to improve continuously the solutions found by the cluster ensembles. As a contribution to this important subject, this paper presents an investigation of five bio-inspired techniques in the optimization of cluster ensembles (Genetic Algorithms, Particle Swarm Optimization, Ant Colony Optimization, Coral Reefs Optimization and Bee Colony Optimization). In this investigation, unlike most of the existing work, an evaluation methodology for assessing three important aspects of cluster ensembles will be presented, assessing robustness, novelty and stability of the consensus partition delivered by different optimization algorithms. In order to evaluate the feasibility of the analyzed techniques, an empirical analysis will be conducted using 20 different problems and applying two different indexes in order to examine its efficiency and feasibility. Our findings indicated that the best population-based optimization method was PSO, followed by CRO, AG, BCO and ACO, for providing robust and stable consensus partitions.

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This work has been financially supported partially by Capes/Brazil.

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Correspondence to Anne Canuto.

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Canuto, A., Neto, A.F., Silva, H.M. et al. Population-based bio-inspired algorithms for cluster ensembles optimization. Nat Comput 19, 515–532 (2020). https://doi.org/10.1007/s11047-018-9682-1

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  • Cluster ensemble
  • Consensus partition
  • Population-based bio-inspired optimization