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Improving Group Search Optimization for Automatic Data Clustering Using Merge and Split Operators

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Intelligent Systems (BRACIS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13653))

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Abstract

The amount of digital data produced daily has increased considerably in the last years. The need for fast and reliable information in real-world applications demands ever more precise algorithms and Data Mining tools, once most of the systems in our daily lives are executed in real-time. Data clustering is one of the most important and primitive activities in Unsupervised Machine Learning, consisting in a fundamental mechanism for exploratory data analysis. Given the complexity of data clustering task, standard clustering methods, such as the partitional algorithms, are easily trapped in local optima solutions, due to their lack of good global searching operators. In this work, three improved Group Search Optimization-based approaches are proposed, based on merge and split heuristics, in the context of Automatic Clustering Analysis: MGSO, SGSO and MSGSO. Group Search Optimization (GSO) is a natural-inspired meta-heuristic, known for its good global search abilities, and mechanisms to escape from local optima points from the problem space. The proposed models attempt to perform both cluster optimization and the determination of the best number of clusters for each dataset, overcoming the limitations of traditional partitional clustering algorithms. The proposed GSO-based models are evaluated through a testing bed composed of nine real-world problems, and compared to six state-of-the-art partitional automatic clustering approaches, include standard GSO. The experimental evaluation has been performed considering five clustering metrics, and both empirical and statistical analysis. The results showed that the proposed MGSO, SGSO and MSGSO algorithms are very promising and reliable while tackling clustering problems.

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Acknowledgements

The authors would like to thank FACEPE, CNPq and CAPES (Brazilian Research Agencies) for their financial support.

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Authors and Affiliations

  1. Departamento de Computação (DC), Universidade Federal Rural de Pernambuco, Recife, PE, Brazil

    Luciano D. S. Pacifico

  2. Centro de Informática, Universidade Federal de Pernambuco, Recife, PE, Brazil

    Teresa B. Ludermir

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  1. Luciano D. S. Pacifico
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  2. Teresa B. Ludermir
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Corresponding author

Correspondence to Luciano D. S. Pacifico .

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Editors and Affiliations

  1. Federal University of Rio Grande do Norte, Natal, Brazil

    João Carlos Xavier-Junior

  2. Federal University of Bahia, Salvador, Brazil

    Ricardo Araújo Rios

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Pacifico, L.D.S., Ludermir, T.B. (2022). Improving Group Search Optimization for Automatic Data Clustering Using Merge and Split Operators. In: Xavier-Junior, J.C., Rios, R.A. (eds) Intelligent Systems. BRACIS 2022. Lecture Notes in Computer Science(), vol 13653. Springer, Cham. https://doi.org/10.1007/978-3-031-21686-2_24

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  • DOI: https://doi.org/10.1007/978-3-031-21686-2_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-21685-5

  • Online ISBN: 978-3-031-21686-2

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