Towards a Unified Symbolic AI Framework for Mining High Utility Itemsets

Amel Hidouri; Badran Raddaoui; Said Jabbour

doi:10.1007/978-3-031-48316-5_11

Towards a Unified Symbolic AI Framework for Mining High Utility Itemsets

Amel Hidouri
, Badran Raddaoui
, Said Jabbour

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

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.

Original language	English
Title of host publication	Information Integration and Web Intelligence - 25th International Conference, iiWAS 2023, Proceedings
Editors	Pari Delir Haghighi, Eric Pardede, Gillian Dobbie, Vithya Yogarajan, Ngurah Agus Sanjaya ER, Gabriele Kotsis, Ismail Khalil
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	77-91
Number of pages	15
ISBN (Print)	9783031483158
DOIs	https://doi.org/10.1007/978-3-031-48316-5_11
Publication status	Published - 1 Jan 2023
Event	25th International Conference on Information Integration and Web Intelligence, iiWAS 2023 - Denpasar, Indonesia Duration: 4 Dec 2023 → 6 Dec 2023

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	14416 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	25th International Conference on Information Integration and Web Intelligence, iiWAS 2023
Country/Territory	Indonesia
City	Denpasar
Period	4/12/23 → 6/12/23

Keywords

Constraints
High Utility Itemsets
Propositional Satisfiability
Symbolic Artificial Intelligence

Access to Document

10.1007/978-3-031-48316-5_11

Cite this

Hidouri, A., Raddaoui, B., & Jabbour, S. (2023). Towards a Unified Symbolic AI Framework for Mining High Utility Itemsets. In P. Delir Haghighi, E. Pardede, G. Dobbie, V. Yogarajan, N. A. S. ER, G. Kotsis, & I. Khalil (Eds.), Information Integration and Web Intelligence - 25th International Conference, iiWAS 2023, Proceedings (pp. 77-91). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14416 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-48316-5_11

Hidouri, Amel ; Raddaoui, Badran ; Jabbour, Said. / Towards a Unified Symbolic AI Framework for Mining High Utility Itemsets. Information Integration and Web Intelligence - 25th International Conference, iiWAS 2023, Proceedings. editor / Pari Delir Haghighi ; Eric Pardede ; Gillian Dobbie ; Vithya Yogarajan ; Ngurah Agus Sanjaya ER ; Gabriele Kotsis ; Ismail Khalil. Springer Science and Business Media Deutschland GmbH, 2023. pp. 77-91 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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Hidouri, A, Raddaoui, B & Jabbour, S 2023, Towards a Unified Symbolic AI Framework for Mining High Utility Itemsets. in P Delir Haghighi, E Pardede, G Dobbie, V Yogarajan, NAS ER, G Kotsis & I Khalil (eds), Information Integration and Web Intelligence - 25th International Conference, iiWAS 2023, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14416 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 77-91, 25th International Conference on Information Integration and Web Intelligence, iiWAS 2023, Denpasar, Indonesia, 4/12/23. https://doi.org/10.1007/978-3-031-48316-5_11

Towards a Unified Symbolic AI Framework for Mining High Utility Itemsets. / Hidouri, Amel; Raddaoui, Badran; Jabbour, Said.
Information Integration and Web Intelligence - 25th International Conference, iiWAS 2023, Proceedings. ed. / Pari Delir Haghighi; Eric Pardede; Gillian Dobbie; Vithya Yogarajan; Ngurah Agus Sanjaya ER; Gabriele Kotsis; Ismail Khalil. Springer Science and Business Media Deutschland GmbH, 2023. p. 77-91 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14416 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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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.

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BT - Information Integration and Web Intelligence - 25th International Conference, iiWAS 2023, Proceedings

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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

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Hidouri A, Raddaoui B, Jabbour S. Towards a Unified Symbolic AI Framework for Mining High Utility Itemsets. In Delir Haghighi P, Pardede E, Dobbie G, Yogarajan V, ER NAS, Kotsis G, Khalil I, editors, Information Integration and Web Intelligence - 25th International Conference, iiWAS 2023, Proceedings. Springer Science and Business Media Deutschland GmbH. 2023. p. 77-91. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-48316-5_11