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FSSD - A Fast and Efficient Algorithm for Subgroup Set Discovery

Adnene Belfodil 1, 2 Aimene Belfodil 1 Anes Bendimerad 1 Philippe Lamarre 2 Céline Robardet 1 Mehdi Kaytoue 1 Marc Plantevit 1 
1 DM2L - Data Mining and Machine Learning
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
2 BD - Base de Données
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : Subgroup discovery (SD) is the task of discovering interpretable patterns in the data that stand out w.r.t. some property of interest. Discovering patterns that accurately discriminate a class from the others is one of the most common SD tasks. Standard approaches of the literature are based on local pattern discovery, which is known to provide an overwhelmingly large number of redundant patterns. To solve this issue, pattern set mining has been proposed: instead of evaluating the quality of patterns separately, one should consider the quality of a pattern set as a whole. The goal is to provide a small pattern set that is diverse and well-discriminant to the target class. In this work, we introduce a novel formulation of the task of diverse subgroup set discovery where both discriminative power and diversity of the subgroup set are incorporated in the same quality measure. We propose an efficient and parameter-free algorithm dubbed FSSD and based on a greedy scheme. FSSD uses several optimization strategies that enable to efficiently provide a high quality pattern set in a short amount of time.
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Submitted on : Friday, November 8, 2019 - 11:50:26 AM
Last modification on : Friday, September 30, 2022 - 11:34:16 AM
Long-term archiving on: : Sunday, February 9, 2020 - 7:38:50 PM


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Adnene Belfodil, Aimene Belfodil, Anes Bendimerad, Philippe Lamarre, Céline Robardet, et al.. FSSD - A Fast and Efficient Algorithm for Subgroup Set Discovery. IEEE International Conference on Data Science and Advanced Analytics (DSAA), Oct 2019, Washington DC, United States. ⟨10.1109/DSAA.2019.00023⟩. ⟨hal-02355503⟩



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