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Improvement of subgroup descriptions in noisy data by detecting exceptions

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Abstract

The presence of noise in datasets to which data mining techniques are applied can greatly reduce the quality and interest of the knowledge extracted. Subgroup discovery is a supervised descriptive rule discovery technique which is not exempt from this problem. The aim of this paper is to improve the descriptions of subgroups previously obtained by any subgroup discovery algorithm in noisy datasets. This is achieved using the post-processing approach of the MEFES algorithm, that first detects exceptions in the input subgroups and then includes those exceptions in the descriptions. The experiments performed in noisy datasets show the suitability of the proposal to improve the quality of the results.

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Notes

  1. http://www.keel.es.

  2. http://sci2s.ugr.es/sicidm/.

  3. http://simidat.ujaen.es/papers/SD-Noisy.

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Acknowledgements

This paper was supported by the Spanish Ministry of Economy and Competitiveness under Project TIN2015-68454-R (FEDER Founds).

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Authors and Affiliations

  1. Department of Computer Science, University of Jaen, 23071, Jaén, Spain

    Pedro González, Ángel Miguel García-Vico & María José del Jesus

  2. Department of Civil Engineering, University of Burgos, 09006, Burgos, Spain

    Cristóbal José Carmona

  3. Leicester School of Pharmacy, De Montfort University, LE1 9BH, Leicester, UK

    Cristóbal José Carmona

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  1. Pedro González
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  2. Ángel Miguel García-Vico
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  3. Cristóbal José Carmona
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  4. María José del Jesus
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Correspondence to Pedro González.

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González, P., García-Vico, Á.M., Carmona, C.J. et al. Improvement of subgroup descriptions in noisy data by detecting exceptions. Prog Artif Intell 7, 55–64 (2018). https://doi.org/10.1007/s13748-017-0131-7

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