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I/F-Race tuned firefly algorithm and particle swarm optimization for K-medoids-based clustering

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Abstract

Clustering is still one of the most common unsupervised learning techniques in data mining since it allows the discovery of meaningful and interesting patterns, knowledge, rules and associations from large-scale datasets. K-medoids, a variant of K-means, is a popular clustering method that attempts to find the optimal combination of K medoids from among a set of potential combinations. It has been successfully applied to solve various real-life problems owing to its simplicity and effectiveness. Nevertheless, due to the exponential number of possible combinations of K medoids, it is extremely challenging to produce the optimal one within a reasonable amount of time. Therefore, in this work, we propose to formulate the problem of K-medoids clustering as an optimization problem and then combine two effective and powerful Swarm Intelligence (SI) algorithms, namely Firefly Algorithm (FA) and Particle Swarm Optimization (PSO), to select the appropriate combination of K medoids. We extensively evaluate the proposed FA-PSO for K-medoids-based clustering, abbreviated as FA-PSO-KMED, using 10 UCI datasets. We first use the Iterated F-Race (I/F-Race) algorithm to determine the optimal parameter settings for FA and PSO. Then, we compare the results of the proposed FA-PSO-KMED with those obtained using the well-known state-of-the-art K-medoids-based clustering algorithms: PAM, CLARA and CLARANS. We also compare the results with 11 popular swarm intelligence algorithms: PSO, ABC, CS, FA, BA, APSO, EHO, HHO, SMA, AO and RSA. Experimental results and statistical analysis show that the proposed FA-PSO-KMED is very promising and demonstrates a significant improvement over the other clustering algorithms.

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Acknowledgements

We would like to express our special thanks of gratitude to the Directorate General for Scientific Research and Technological Development (DGRSDT), for the support of this work under the grant number C0662300.

Funding

This work was supported by the Directorate General for Scientific Research and Technological Development (DGRSDT) under the grant number C0662300.

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Authors and Affiliations

  1. Laboratory for Research in Artificial Intelligence, USTHB, Algiers, Algeria

    Ilyes Khennak, Habiba Drias, Yassine Drias, Faysal Bendakir & Samy Hamdi

  2. Algiers University, Algiers, Algeria

    Yassine Drias

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  1. Ilyes Khennak
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Contributions

Supervision   Habiba Drias. Concept and Design   Ilyes Khennak, Faysal Bendakir and Samy Hamdi. Data Collection and/or Processing   Ilyes Khennak, Faysal Bendakir and Samy Hamdi. Analysis and/or Interpretation   Ilyes Khennak. Literature Search   Ilyes Khennak. Manuscript Writing   Ilyes Khennak. Critical Review   Habiba Drias and Yassine Drias

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Correspondence to Ilyes Khennak.

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Khennak, I., Drias, H., Drias, Y. et al. I/F-Race tuned firefly algorithm and particle swarm optimization for K-medoids-based clustering. Evol. Intel. 16, 351–373 (2023). https://doi.org/10.1007/s12065-022-00794-z

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