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Searching for the most significant rules: an evolutionary approach for subgroup discovery

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Abstract

In this paper, a new genetic algorithm (GAR-SD\(^{+})\) for subgroup discovery tasks is described. The main feature of this new method is that it can work with both discrete and continuous attributes without previous discretization. The ranges of numeric attributes are obtained in the rules induction process itself. In this way, we ensure that these intervals are the most suitable for maximizing the quality measures. An experimental study was carried out to verify the performance of the method. GAR-SD\(^{+}\) was compared with other subgroup discovery methods by evaluating certain measures (such as number of rules, number of attributes, significance, unusualness, support and confidence). For subgroup discovery tasks, GAR-SD\(^{+}\) obtained good results compared with existing algorithms.

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Acknowledgments

This work was partially funded by the Regional Government of Andalusia (Junta de Andalucía, Grant Number TIC-7629) and the Spanish Ministry of Economy and Competitiveness (Grant Number TIN2013-47153-C3-2-R).

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Authors and Affiliations

  1. Escuela Técnica Superior de Ingeniería, Universidad de Huelva, Carretera Palos de la Frontera SN, Huelva, Spain

    Victoria Pachón, Jacinto Mata & Juan Luis Domínguez

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  1. Victoria Pachón
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  2. Jacinto Mata
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  3. Juan Luis Domínguez
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Correspondence to Jacinto Mata.

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The authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest (such as honoraria; educational grants; participation in speakers? bureaus; membership, employment, consultancies, stock ownership, or other equity interest; and expert testimony or patent-licensing arrangements), or non-financial inter-est (such as personal or professional relationships, affiliations, knowledge or beliefs) in the subject matter or materials discussed in this manuscript.

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Communicated by V. Loia.

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Pachón, V., Mata, J. & Domínguez, J.L. Searching for the most significant rules: an evolutionary approach for subgroup discovery. Soft Comput 21, 2609–2618 (2017). https://doi.org/10.1007/s00500-015-1961-5

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