%0 Journal Article
%9 ACL : Articles dans des revues avec comité de lecture répertoriées par l'AERES
%A Amer-Yahia, S. 
%A Berti-Equille, Laure
%A Chibah, A.
%T A framework for statistically-sound customer segment search
%B 2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA)
%C Piscataway
%D 2021
%L fdi:010085545
%G ENG
%I IEEE
%@ 978-1-6654-2100-3
%M ISI:000783799800060
%P 1-10
%R 10.1109/DSAA53316.2021.9564199
%U https://www.documentation.ird.fr/hor/fdi:010085545
%> https://www.documentation.ird.fr/intranet/publi/2023-02/010085545.pdf
%W Horizon (IRD)
%X We develop S4, a Statistically-Sound Segment Search framework that combines principled data partitioning and sound statistical testing to verify common hypotheses in retail data and return interpretable customer data segments. Our framework accommodates one-sample, two-sample, and multiple-sample testing, to provide various aggregations and comparisons of customer transactions. To control the proportion of false discoveries in multiple hypothesis testing, we enforce an FDR-controlling procedure and formulate a unified optimization problem that returns customer data segments that satisfy the test for a given significance level, maximize coverage of the input data, and are within a risk capital. We develop a greedy algorithm to explore different data partitions and test multiple hypotheses in a sound manner. Our extensive experiments on four retail data sets examine the interaction between significance, risk and coverage, and demonstrate the expressivity, usefulness, and scalability of S4 in practice.
%B International Conference on Data Science and Advanced Analytics (DSAA)
%8 2021/10/06-09
%$ 122 ; 020