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Evaluating Data Characterization Measures for Clustering Problems in Meta-learning

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Neural Information Processing (ICONIP 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 13108))

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Abstract

An accurate data characterization is essential for a reliable selection of clustering algorithms via meta-learning. This work evaluates a set of measures for characterizing clustering problems using beta regression and two well-known machine learning regression techniques as meta-models. We have observed a subset of meta-features which demonstrates greater resourcefulness to characterize the clustering datasets. In addition, secondary findings made it possible to verify the direction and magnitude of the influence and the importance of such measures in predicting the performance of the algorithms under analysis.

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Acknowledgements

To the Brazilian research agency CNPq.

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Authors and Affiliations

  1. Instituto Tecnológico de Aeronáutica, São José dos Campos, SP, 12228-900, Brazil

    Luiz Henrique dos S. Fernandes & Ana C. Lorena

  2. Université d’Orléans, Orléans, 45100, France

    Marcilio C. P. de Souto

Authors
  1. Luiz Henrique dos S. Fernandes
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  2. Marcilio C. P. de Souto
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  3. Ana C. Lorena
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Corresponding author

Correspondence to Luiz Henrique dos S. Fernandes .

Editor information

Editors and Affiliations

  1. Sampoerna University, Jakarta, Indonesia

    Teddy Mantoro

  2. Kyungpook National University, Daegu, Korea (Republic of)

    Minho Lee

  3. Sampoerna University, Jakarta, Indonesia

    Media Anugerah Ayu

  4. Murdoch University, Murdoch, WA, Australia

    Kok Wai Wong

  5. Universitas Indonesia, Depok, Indonesia

    Achmad Nizar Hidayanto

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Fernandes, L.H.d.S., de Souto, M.C.P., Lorena, A.C. (2021). Evaluating Data Characterization Measures for Clustering Problems in Meta-learning. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds) Neural Information Processing. ICONIP 2021. Lecture Notes in Computer Science(), vol 13108. Springer, Cham. https://doi.org/10.1007/978-3-030-92185-9_51

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  • DOI: https://doi.org/10.1007/978-3-030-92185-9_51

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-92184-2

  • Online ISBN: 978-3-030-92185-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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