Itemset Mining with Penalties

Abstract : We introduce a preferences-based itemset mining framework. Preferences are encoded by a penalty function over the transactions in a database. We define an itemset mining problem where we associate to each transaction a penalty value. This problem consists in generating the frequent itemsets with a maximum penalty threshold. We then provide a propositional satisfiability based encoding. We extend the previous problem with a penalty function over items, where we use two maximum penalty thresholds, over the transactions and over the items. In this setting, computing the optimum itemsets corresponds to computing Pareto front. The experimental evaluation on real world data shows the feasibility of our approach.
Type de document :
Communication dans un congrès
ICTAI: International Conference on Tools with Artificial Intelligence, Nov 2016, San Jose, CA, United States. 28th IEEE International Conference on Tools with Artificial Intelligence, 2017, 〈10.1109/ICTAI.2016.0148〉
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-01799451
Contributeur : Souhila Kaci <>
Soumis le : jeudi 24 mai 2018 - 17:24:32
Dernière modification le : mercredi 30 mai 2018 - 01:16:24

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Said Jabbour, Souhila Kaci, Lakhdar Sais, Yakoub Salhi. Itemset Mining with Penalties. ICTAI: International Conference on Tools with Artificial Intelligence, Nov 2016, San Jose, CA, United States. 28th IEEE International Conference on Tools with Artificial Intelligence, 2017, 〈10.1109/ICTAI.2016.0148〉. 〈lirmm-01799451〉

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