A Symbolic Approach to Computing Disjunctive Association Rules from Data
Said Jabbour, Badran Raddaoui, Lakhdar Sais
Abstract
Association rule mining is one of the well-studied and most important knowledge discovery task in data mining. In this paper, we first introduce the k-disjunctive support based itemset, a generalization of the traditional model of itemset by allowing the absence of up to k items in each transaction matching the itemset. Then, to discover more expressive rules from data, we define the concept of (k, k′)-disjunctive support based association rules by considering the antecedent and the consequent of the rule as k-disjunctive and k′-disjunctive support based itemsets, respectively. Second, we provide a polynomial-time reduction of both the problems of mining k-disjunctive support based itemsets and (k, k′)-disjunctive support based association rules to the propositional satisfiability model enumeration task. Finally, we show through an extensive campaign of experiments on several popular real-life datasets the efficiency of our proposed approach
BibTeX
@inproceedings{ijcai2023p237,
title = {A Symbolic Approach to Computing Disjunctive Association Rules from Data},
author = {Jabbour, Said and Raddaoui, Badran and Sais, Lakhdar},
booktitle = {Proceedings of the Thirty-Second International Joint Conference on
Artificial Intelligence, {IJCAI-23}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Edith Elkind},
pages = {2133--2141},
year = {2023},
month = {8},
note = {Main Track},
doi = {10.24963/ijcai.2023/237},
url = {https://doi.org/10.24963/ijcai.2023/237},
}