Identifying exceptional (dis)agreement between groups

Adnene Belfodil 1, 2 Sylvie Cazalens 1 Philippe Lamarre 1 Marc Plantevit 2
1 BD - Base de Données
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
2 DM2L - Data Mining and Machine Learning
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : Under the term behavioral data, we consider any type of data featuring individuals performing observable actions on entities. For instance, voting data depict parliamentarians who express their votes w.r.t. legislative procedures. In this work, we address the problem of discovering exceptional (dis)agreement patterns in such data, i.e., groups of individuals that exhibit an unexpected (dis)agreement under specific contexts compared to what is observed in overall terms. To tackle this problem, we design a generic approach , rooted in the Subgroup Discovery/Exceptional Model Mining framework , which enables the discovery of such patterns in two different ways. A branch-and-bound algorithm ensures an efficient exhaustive search of the underlying search space by leveraging closure operators and optimistic estimates on the interestingness measures. A second algorithm abandons the completeness by using a sampling paradigm which provides an alternative when an exhaustive search approach becomes unfeasible. To illustrate the usefulness of discovering exceptional (dis)agreement patterns, we report a comprehensive experimental study on four real-world datasets relevant to three different application domains: political analysis, rating data analysis and healthcare surveillance.
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Submitted on : Thursday, November 28, 2019 - 6:22:48 AM
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Adnene Belfodil, Sylvie Cazalens, Philippe Lamarre, Marc Plantevit. Identifying exceptional (dis)agreement between groups. Data Mining and Knowledge Discovery, Springer, 2019, ⟨10.1007/s10618-019-00665-9⟩. ⟨hal-02383776⟩

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