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Supervised Local Pattern Mining

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Pattern Mining with Evolutionary Algorithms

Abstract

Pattern mining is considered as a really interesting task for the extraction of hidden knowledge in the form of patterns. The extraction of such subsequences, substructures or itemsets that represent any type of homogeneity and regularity in data has been carried out from unlabeled data. However, there are many research areas that aim at discovering patterns in the form of rules induced from labeled data. Hence, it is interesting to discover patterns and associations from a supervised point of view since a single item or a set of them can be considered as distinctive. This task, which is known as supervised local pattern mining, is described in this chapter, including different areas such as contrast set mining, emerging pattern mining, and subgroup discovery. This chapter is mainly focused on the subgroup discovery task, which is widely known in the field of supervised local pattern mining. Here, an exhaustive description about this task is provided, including some important quality measures in the field. Then, this chapter includes different evolutionary approaches for subgroup discovery. Finally, this chapter includes an analysis of other different approaches proposed for mining and quantifying local patterns.

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Authors and Affiliations

  1. Department of Computer Science and Numerical Analysis, 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. (2016). Supervised Local Pattern Mining. In: Pattern Mining with Evolutionary Algorithms. Springer, Cham. https://doi.org/10.1007/978-3-319-33858-3_7

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  • DOI: https://doi.org/10.1007/978-3-319-33858-3_7

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