Anytime Subgroup Discovery in Numerical Domains with Guarantees

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11052)


Subgroup discovery is the task of discovering patterns that accurately discriminate a class label from the others. Existing approaches can uncover such patterns either through an exhaustive or an approximate exploration of the pattern search space. However, an exhaustive exploration is generally unfeasible whereas approximate approaches do not provide guarantees bounding the error of the best pattern quality nor the exploration progression (“How far are we of an exhaustive search”). We design here an algorithm for mining numerical data with three key properties w.r.t. the state of the art: (i) It yields progressively interval patterns whose quality improves over time; (ii) It can be interrupted anytime and always gives a guarantee bounding the error on the top pattern quality and (iii) It always bounds a distance to the exhaustive exploration. After reporting experimentations showing the effectiveness of our method, we discuss its generalization to other kinds of patterns. Code related to this paper is available at:


Subgroup discovery Anytime algorithms Discretization 



This work has been partially supported by the project ContentCheck ANR-15-CE23-0025 funded by the French National Research Agency, the Association Nationale Recherche Technologie (ANRt) French program and the APRC Conf Pap - CNRS project. The authors would like to thank the reviewers for their valuable remarks. They also warmly thank Loïc Cerf, Marc Plantevit and Anes Bendimerad for interesting discussions.

Supplementary material

478890_1_En_30_MOESM1_ESM.pdf (728 kb)
Supplementary material 1 (pdf 728 KB)


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Univ Lyon, INSA Lyon, CNRS, LIRIS UMR 5205LyonFrance
  2. 2.Mobile Devices IngénierieVillejuifFrance
  3. 3.InfologicBourg-Lès-ValenceFrance

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