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Subgroup Discovery

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Supervised Descriptive Pattern Mining

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Abstract

Subgroup discovery is the most well-known task within the supervised descriptive pattern mining field. It aims at discovering patterns in the form of rules induced from labeled data. This chapter therefore introduces the subgroup discovery problem and also describes the main differences with regard to classification and clustering tasks. Additionally, it provides a good description about similarities and differences with respect to other well-known tasks within the supervised descriptive pattern mining field such as contrast set mining and emerging pattern mining. Finally, the most widely used metrics in this field as well as important approaches to perform this task are analysed.

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Authors and Affiliations

  1. Computer Science, University of Cordoba, Cordoba, Spain

    Sebastián Ventura & José María Luna

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  1. Sebastián Ventura
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  2. José María Luna
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Ventura, S., Luna, J.M. (2018). Subgroup Discovery. In: Supervised Descriptive Pattern Mining. Springer, Cham. https://doi.org/10.1007/978-3-319-98140-6_4

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  • DOI: https://doi.org/10.1007/978-3-319-98140-6_4

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  • Print ISBN: 978-3-319-98139-0

  • Online ISBN: 978-3-319-98140-6

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