research-article

Constraint programming for itemset mining

Authors:

Siegfried NijssenAuthors Info & Claims

KDD '08: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 204 - 212

https://doi.org/10.1145/1401890.1401919

Published: 24 August 2008 Publication History

Abstract

The relationship between constraint-based mining and constraint programming is explored by showing how the typical constraints used in pattern mining can be formulated for use in constraint programming environments. The resulting framework is surprisingly flexible and allows us to combine a wide range of mining constraints in different ways. We implement this approach in off-the-shelf constraint programming systems and evaluate it empirically. The results show that the approach is not only very expressive, but also works well on complex benchmark problems.

References

[1]

R. Agrawal, H. Mannila, R. Srikant, H. Toivonen, and A. I. Verkamo. Fast discovery of association rules. In Advances in Knowledge Discovery and Data Mining, pages 307--328. AAAI Press, 1996.

Digital Library

[2]

K. R. Apt and M. Wallace. Constraint Logic Programming using Eclipse. Cambridge University Press, New York, NY, USA, 2007.

Digital Library

[3]

F. Bonchi and C. Lucchese. On closed constrained frequent pattern mining. In ICDM, pages 35--42. IEEE Computer Society, 2004.

Digital Library

[4]

F. Bonchi and C. Lucchese. Extending the state-of-the-art of constraint-based pattern discovery. Data Knowl. Eng., 60(2):377--399, 2007.

Digital Library

[5]

C. Bucila, J. Gehrke, D. Kifer, and W. M. White. Dualminer: A dual-pruning algorithm for itemsets with constraints. Data Min. Knowl. Discov., 7(3):241--272, 2003.

Digital Library

[6]

D. Burdick, M. Calimlim, J. Flannick, J. Gehrke, and T. Yiu. Mafia: A maximal frequent itemset algorithm. IEEE Trans. Knowl. Data Eng., 17(11):1490--1504, 2005.

Digital Library

[7]

J. Cheng, Y. Ke, and W. Ng. δ-tolerance closed frequent itemsets. In ICDM '06: Proceedings of the Sixth International Conference on Data Mining, pages 139--148, 2006.

Digital Library

[8]

G. Dong and J. Li. Efficient mining of emerging patterns: Discovering trends and differences. In KDD, pages 43--52, 1999.

Digital Library

[9]

E. Frank and I. H. Witten. Using a permutation test for attribute selection in decision trees. In Proc. 15th International Conf. on Machine Learning, pages 152--160, 1998.

Digital Library

[10]

B. Goethals and M. J. Zaki. Advances in frequent itemset mining implementations: report on FIMI'03. In SIGKDD Explorations Newsletter, volume 6, pages 109--117, 2004.

Digital Library

[11]

J. Han, J. Pei, and Y. Yin. Mining frequent patterns without candidate generation. In Proceedings of the ACM SIGMOD International Conference on Management of Data, pages 1--12, 2000.

Digital Library

[12]

J. Pei, J. Han, and L. V. S. Lakshmanan. Mining frequent item sets with convertible constraints. In Proceedings of the IEEE International Conference on Data Engineering (ICDE), pages 433--442, 2001.

Digital Library

[13]

F. Rossi, P. van Beek, and T. Walsh. Handbook of Constraint Programming (Foundations of Artificial Intelligence). Elsevier Science Inc., 2006.

Digital Library

[14]

C. Schulte and P. J. Stuckey. Efficient constraint propagation engines. Transactions on Programming Languages and Systems, 2008. To appear.

Digital Library

[15]

T. Uno, M. Kiyomi, and H. Arimura. Lcm ver.3: collaboration of array, bitmap and prefix tree for frequent itemset mining. In OSDM '05: Proceedings of the 1st international workshop on open source data mining, pages 77--86, 2005.

Digital Library

[16]

M. J. Zaki, S. Parthasarathy, M. Ogihara, and W. Li. New algorithms for fast discovery of association rules. In KDD, pages 283--286, 1997.

Digital Library

Cited By

Baaj IBouraoui ZCornuéjols ADenœux TDestercke SDubois DLesot MMarques-Silva JMengin JPrade HSchockaert SSerrurier MStrauss OVrain C(2024)Synergies Between Machine Learning and Reasoning - An Introduction by the Kay R. Amel groupInternational Journal of Approximate Reasoning10.1016/j.ijar.2024.109206(109206)Online publication date: Apr-2024
https://doi.org/10.1016/j.ijar.2024.109206
Dao TVrain C(2024)A review on declarative approaches for constrained clusteringInternational Journal of Approximate Reasoning10.1016/j.ijar.2024.109135(109135)Online publication date: Feb-2024
https://doi.org/10.1016/j.ijar.2024.109135
Hien AAribi NLoudni SLebbah YOuali AZimmermann A(2024)Mining diverse sets of patterns with constraint programming using the pairwise Jaccard similarity relaxationConstraints10.1007/s10601-024-09373-829:1-2(80-111)Online publication date: 2-Oct-2024
https://doi.org/10.1007/s10601-024-09373-8
Show More Cited By

Index Terms

Constraint programming for itemset mining
1. Information systems
  1. Information systems applications
    1. Data mining
2. Theory of computation
  1. Logic
    1. Constraint and logic programming

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cover image ACM Conferences

KDD '08: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining

August 2008

1116 pages

ISBN:9781605581934

DOI:10.1145/1401890

General Chair:
Ying Li
Microsoft adCenter Labs
,
Program Chairs:
Bing Liu
University of Illinois at Chicago
,
Sunita Sarawagi
Indian Institute of Technology, Bombay

Copyright © 2008 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 24 August 2008

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KDD08

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KDD08: The 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 24 - 27, 2008

Nevada, Las Vegas, USA

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KDD '08 Paper Acceptance Rate 118 of 593 submissions, 20%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Baaj IBouraoui ZCornuéjols ADenœux TDestercke SDubois DLesot MMarques-Silva JMengin JPrade HSchockaert SSerrurier MStrauss OVrain C(2024)Synergies Between Machine Learning and Reasoning - An Introduction by the Kay R. Amel groupInternational Journal of Approximate Reasoning10.1016/j.ijar.2024.109206(109206)Online publication date: Apr-2024
https://doi.org/10.1016/j.ijar.2024.109206
Dao TVrain C(2024)A review on declarative approaches for constrained clusteringInternational Journal of Approximate Reasoning10.1016/j.ijar.2024.109135(109135)Online publication date: Feb-2024
https://doi.org/10.1016/j.ijar.2024.109135
Hien AAribi NLoudni SLebbah YOuali AZimmermann A(2024)Mining diverse sets of patterns with constraint programming using the pairwise Jaccard similarity relaxationConstraints10.1007/s10601-024-09373-829:1-2(80-111)Online publication date: 2-Oct-2024
https://doi.org/10.1007/s10601-024-09373-8
Jabbour SRaddaoui BSais LElkind E(2023)A symbolic approach to computing disjunctive association rules from dataProceedings of the Thirty-Second International Joint Conference on Artificial Intelligence10.24963/ijcai.2023/237(2133-2141)Online publication date: 19-Aug-2023
https://dl.acm.org/doi/10.24963/ijcai.2023/237
Koptelov MZimmermann ABoizumault PBureau RLamotte J(2023)Explanations for Itemset Mining by Constraint Programming: A Case Study Using ChEMBL DataAdvances in Intelligent Data Analysis XXI10.1007/978-3-031-30047-9_17(208-221)Online publication date: 1-Apr-2023
https://doi.org/10.1007/978-3-031-30047-9_17
Alòs JAnsótegui CTorres E(2022)Interpretable decision trees through MaxSATArtificial Intelligence Review10.1007/s10462-022-10377-056:8(8303-8323)Online publication date: 27-Dec-2022
https://dl.acm.org/doi/10.1007/s10462-022-10377-0
Le Van Tvan Leeuwen MNijssen SFierro AMarchal KDe Raedt L(2022)Ranked TilingMachine Learning and Knowledge Discovery in Databases10.1007/978-3-662-44851-9_7(98-113)Online publication date: 10-Mar-2022
https://dl.acm.org/doi/10.1007/978-3-662-44851-9_7
Martin-Prin JDlala ITravers NJabbour S(2022)A Distributed SAT-Based Framework for Closed Frequent Itemset MiningAdvanced Data Mining and Applications10.1007/978-3-031-22137-8_31(419-433)Online publication date: 24-Nov-2022
https://doi.org/10.1007/978-3-031-22137-8_31
Belaid MLazaar N(2021)Constraint Programming for Itemset Mining with Multiple Minimum Supports2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI)10.1109/ICTAI52525.2021.00095(598-603)Online publication date: Nov-2021
https://doi.org/10.1109/ICTAI52525.2021.00095
Hidouri AJabbour SRaddaoui BBen Yaghlane B(2021)Mining Closed High Utility Itemsets based on Propositional SatisfiabilityData & Knowledge Engineering10.1016/j.datak.2021.101927136:COnline publication date: 1-Nov-2021
https://dl.acm.org/doi/10.1016/j.datak.2021.101927
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