TY - GEN
T1 - Towards a Unified Symbolic AI Framework for Mining High Utility Itemsets
AU - Hidouri, Amel
AU - Raddaoui, Badran
AU - Jabbour, Said
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023/1/1
Y1 - 2023/1/1
N2 - This paper deals with the task of mining high utility itemsets. The proposed approach presents a unified framework for efficiently mining high utility patterns from transaction databases while handling effectively various condensed representations. In addition, this approach offers a way to integrate multiple constraints, including closedness, minimality, and maximality, while maintaining flexibility in the mining process. This allows to significantly enhance the efficiency and effectiveness of mining high utility patterns, making it a valuable tool for various data mining applications. Finally, we show through an extensive campaign of experiments on several popular real-life datasets the efficiency of our proposed approach.
AB - This paper deals with the task of mining high utility itemsets. The proposed approach presents a unified framework for efficiently mining high utility patterns from transaction databases while handling effectively various condensed representations. In addition, this approach offers a way to integrate multiple constraints, including closedness, minimality, and maximality, while maintaining flexibility in the mining process. This allows to significantly enhance the efficiency and effectiveness of mining high utility patterns, making it a valuable tool for various data mining applications. Finally, we show through an extensive campaign of experiments on several popular real-life datasets the efficiency of our proposed approach.
KW - Constraints
KW - High Utility Itemsets
KW - Propositional Satisfiability
KW - Symbolic Artificial Intelligence
U2 - 10.1007/978-3-031-48316-5_11
DO - 10.1007/978-3-031-48316-5_11
M3 - Conference contribution
AN - SCOPUS:85178664915
SN - 9783031483158
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 77
EP - 91
BT - Information Integration and Web Intelligence - 25th International Conference, iiWAS 2023, Proceedings
A2 - Delir Haghighi, Pari
A2 - Pardede, Eric
A2 - Dobbie, Gillian
A2 - Yogarajan, Vithya
A2 - ER, Ngurah Agus Sanjaya
A2 - Kotsis, Gabriele
A2 - Khalil, Ismail
PB - Springer Science and Business Media Deutschland GmbH
T2 - 25th International Conference on Information Integration and Web Intelligence, iiWAS 2023
Y2 - 4 December 2023 through 6 December 2023
ER -