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Evaluation of Relative Indexes for Multi-objective Clustering

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Hybrid Artificial Intelligent Systems (HAIS 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9121))

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Abstract

One of the biggest challenges in clustering is finding a robust and versatile criterion to evaluate the quality of clustering results. In this paper, we investigate the extent to which unsupervised criteria can be used to obtain clusters highly correlated to external labels. We show that the usefulness of these criteria is data-dependent and for most data sets multiple criteria are required in order to identify the best performing clustering algorithm. We present a multi-objective evolutionary clustering algorithm capable of finding a set of high-quality solutions. For the real world data sets examined the Pareto front can offer better clusterings than simply optimizing a single unsupervised criterion.

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Acknowledgements

We would like to thank Petr Bart\(\mathop {\mathrm{{u}}}\limits ^{\tiny \circ }\)něk, Ph.D. from the IMG CAS institute for supporting our research and letting us publish all details of our work. This research is partially supported by CTU grant SGS15/117/OHK3/1T/18 New data processing methods for data mining and Program NPU I (LO1419) by Ministry of Education, Youth and Sports of Czech Republic.

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Authors and Affiliations

  1. Faculty of Information Technology, Czech Technical University in Prague, Thakurova 9, Prague 6, Czech Republic

    Tomáš Bartoň & Pavel Kordík

  2. Institute of Molecular Genetics of the ASCR, V. V. I., Vídeňská 1083, 142 20, Prague 4, Czech Republic

    Tomáš Bartoň

Authors
  1. Tomáš Bartoň
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  2. Pavel Kordík
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Corresponding author

Correspondence to Tomáš Bartoň .

Editor information

Editors and Affiliations

  1. University of Deusto, Bilbao, Spain

    Enrique Onieva

  2. University of Deusto, Bilbao, Spain

    Igor Santos

  3. University of Deusto, Bilbao, Spain

    Eneko Osaba

  4. Universidad de Salamanca, Salamanca, Spain

    Héctor Quintián

  5. Universidad de Salamanca, Salamanca, Spain

    Emilio Corchado

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Bartoň, T., Kordík, P. (2015). Evaluation of Relative Indexes for Multi-objective Clustering. In: Onieva, E., Santos, I., Osaba, E., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2015. Lecture Notes in Computer Science(), vol 9121. Springer, Cham. https://doi.org/10.1007/978-3-319-19644-2_39

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  • DOI: https://doi.org/10.1007/978-3-319-19644-2_39

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19643-5

  • Online ISBN: 978-3-319-19644-2

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