Constraint Programming for Mining Borders of Frequent Itemsets
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence. Main track. Pages 1064-1070.
https://doi.org/10.24963/ijcai.2019/149
Keywords
- Constraints and SAT: Global Constraints
Abstract
Frequent itemset mining is one of the most studied tasks in knowledge discovery. It is often reduced to mining the positive border of frequent itemsets, i.e. maximal frequent itemsets. Infrequent itemset mining, on the other hand, can be reduced to mining the negative border, i.e. minimal infrequent itemsets. We propose a generic framework based on constraint programming to mine both borders of frequent itemsets.One can easily decide which border to mine by setting a simple parameter. For this, we introduce two new global constraints, FREQUENTSUBS and INFREQUENTSUPERS, with complete polynomial propagators. We then consider the problem of mining borders with additional constraints. We prove that this problem is coNP-hard, ruling out the hope for the existence of a single CSP solving this problem (unless coNP ⊆ NP).
@inproceedings{ijcai2019p149,
title = {Constraint Programming for Mining Borders of Frequent Itemsets},
author = {Belaid, Mohamed-Bachir and Bessiere, Christian and Lazaar, Nadjib},
booktitle = {Proceedings of the Twenty-Eighth International Joint Conference on
Artificial Intelligence, {IJCAI-19}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Sarit Kraus},
pages = {1064--1070},
year = {2019},
month = {8},
note = {Main track},
doi = {10.24963/ijcai.2019/149},
url = {https://doi.org/10.24963/ijcai.2019/149},
}
TY - CPAPER TI - Constraint Programming for Mining Borders of Frequent Itemsets AU - Belaid, Mohamed-Bachir AU - Bessiere, Christian AU - Lazaar, Nadjib T2 - Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI-19 PB - International Joint Conferences on Artificial Intelligence Organization PY - 2019 SP - 1064 EP - 1070 DO - 10.24963/ijcai.2019/149 UR - https://doi.org/10.24963/ijcai.2019/149 ER -Copied
Machine-readable versions of this paper, for tools and AI agents: