research-article

Mining Negatives Association Rules Using Constraints

Authors:

Fatima Ezzahra El Mazouri,

Lakhdar SaisAuthors Info & Claims

Procedia Computer Science, Volume 127, Issue C

Pages 481 - 488

https://doi.org/10.1016/j.procs.2018.01.146

Published: 01 May 2018 Publication History

Abstract

Pattern discovery techniques, such as association rule discovery is one of the fundamental problem in data mining. Usually the task is limited to positive rules of the form X Y when X and Y are susbets of items. To enlarge the knowledge discovery from data. Many works pointed out that other rules can be mined linking the present items in transactions with the missing ones designed as negative rules. To mine the most relevant negative rules, the mining task of negative association rules is often coupled with new measure such as lift or conviction to limit the set of extracted association rules.In this work we address the problem of mining strong negative rules by extending the SAT-Based approach proposed in 1. We show that the conviction constraint leads to a non-linear constraints that have to be managed efficiently to prune the search space. Experiments results explore the efficiency of our new approach.

References

[1]

A. Boudane, S. Jabbour, L. Sais, Y. Salhi, A sat-based approach for mining association rules, Proceedings of IJCAI, 16 (2016) 2472-2478.

Digital Library

[2]

R. Agrawal, T. Imielinski, A. Swami, Mining association rules between sets of items in large databases, Proceedings of SIGMOD, 93 (1993) 207-216.

Digital Library

[3]

T. Guns, S. Nijssen, L.D. Raedt, Itemset mining: A constraint programming perspective, Artif. Intell., 175 (2011) 1951-1983.

Digital Library

[4]

T. Guns, S. Nijssen, L.D. Raedt, K-pattern set mining under constraints, IEEE Trans. Knowl. Data Eng., 25 (2013) 402-418.

Digital Library

[5]

M. Jrvisalo, Springer, 2011.

[6]

M. Khiari, P. Boizumault, B. Crmilleux, Springer, 2010.

[7]

J. Mtivier, P. Boizumault, B. Crmilleux, M. Khiari, S. Loudni, A constraint language for declarative pattern discovery, Proceedings of SAC, 12 (2012) 119-125.

Digital Library

[8]

N. Lazaar, Y. Lebbah, S. Loudni, M. Maamar, V. Lemire, C. Bessiere, P. Boizumault, A global constraint for closed frequent pattern mining, in: Principles and Practice of Constraint Programming - 22nd International Conference, CP 2016, Toulouse, France, September 5-9, 2016, Proceedings, 2016, pp. 333349.

[9]

M. Morzy, Efficient mining of dissociation rules, in: Data Warehousing and Knowledge Discovery, 8th International Conference, DaWaK 2006, Krakow, Poland, September 4-8, 2006, Proceedings., 2006, pp. 228237.

Digital Library

[10]

L. Szathmary, P. Valtchev, A. Napoli, Finding minimal rare itemsets and rare association rules, in: Knowledge Science, Engineering and Management, 4th International Conference, KSEM 2010, Belfast, Northern Ireland, UK, September 1-3, 2010. Proceedings, 2010, pp. 1627.

Digital Library

[11]

M. Delgado, M.D. Ruiz, D. Snchez, J. Serrano, A formal model for mining fuzzy rules using the RL representation theory, Inf. Sci., 181 (2011) 5194-5213.

Digital Library

[12]

A. Savasere, E. Omiecinski, S. B. Navathe, Mining for strong negative associations in a large database of customer transactions, in: Proceedings of the Fourteenth International Conference on Data Engineering, Orlando, Florida, USA, February 23-27, 1998, 1998, pp. 494502.

Digital Library

[13]

X. Dong, Z. Niu, X. Shi, X. Zhang, D. Zhu, Mining both positive and negative association rules from frequent and infrequent itemsets, in: Advanced Data Mining and Applications, Third International Conference, ADMA 2007, Harbin, China, August 6-8, 2007, Proceedings, 2007, pp. 122133.

Digital Library

[14]

X. Wu, C. Zhang, S. Zhang, Efficient mining of both positive and negative association rules, ACM Trans. Inf. Syst., 22 (2004) 381-405.

Digital Library

[15]

M.J.A. Berry, G. Linoff, Wiley computer publishing, Wiley, 1997.

[16]

S. Brin, R. Motwani, J. D. Ullman, S. Tsur, Dynamic itemset counting and implication rules for market basket data, in: SIGMOD 1997, Proceedings ACM SIGMOD International Conference on Management of Data, May 13-15, 1997, Tucson, Arizona, USA., 1997, pp. 255 264.

Digital Library

[17]

M. Davis, G. Logemann, D.W. Loveland, A machine program for theorem-proving., Communications of the ACM, 5 (1962) 394-397.

Digital Library

[18]

J. P. Marques-Silva, K. A. Sakallah, GRASP - A New Search Algorithm for Satisfiability, in: Proceedings of IEEE/ACM CAD, 1996, pp. 220227.

Digital Library

[19]

L. Zhang, C. F. Madigan, M. W. Moskewicz, S. Malik, Efficient Conflict Driven Learning in Boolean Satisfiability Solver, in: IEEE/ACM CAD2001, 2001, pp. 279285.

Digital Library

[20]

S. Jabbour, J. Lonlac, L. Sais, Y. Salhi, Extending modern sat solvers for models enumeration, in: Proceedings of the 11th IEEE International Conference on Information Reuse and Integration (IEEE-IRI14), San Francisco, September 13-15th, 2014, pp. 803810.

[21]

S. Jabbour, L. Sais, Y. Salhi, The top-k frequent closed itemset mining using top-k sat problem, Proceedings of ECML/PKDD, 13 (2013) 403-418.

Digital Library

Cited By

Maggi FMarrella APatrizi FSkydanienko V(2023)Data-Aware Declarative Process Mining with SATACM Transactions on Intelligent Systems and Technology10.1145/360010614:4(1-26)Online publication date: 10-Aug-2023
https://dl.acm.org/doi/10.1145/3600106
Shen HLiu TZhang Y(2020)Discovery of Learning Path Based on Bayesian Network Association Rule AlgorithmInternational Journal of Distance Education Technologies10.4018/IJDET.202001010418:1(65-82)Online publication date: 1-Oct-2020
https://dl.acm.org/doi/10.4018/IJDET.2020010104
Liu MYang ZJiang JYang K(2020)Correlation Analysis for Complex Equipment Systems Based on Multi-objective Association Rule Mining2020 IEEE 15th International Conference of System of Systems Engineering (SoSE)10.1109/SoSE50414.2020.9130523(337-342)Online publication date: 2-Jun-2020
https://dl.acm.org/doi/10.1109/SoSE50414.2020.9130523

Index Terms

Mining Negatives Association Rules Using Constraints
1. Information systems
  1. Information systems applications
    1. Data mining

Index terms have been assigned to the content through auto-classification.

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cover image Procedia Computer Science

Procedia Computer Science Volume 127, Issue C

May 2018

552 pages

ISSN:1877-0509

EISSN:1877-0509

Issue’s Table of Contents

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 May 2018

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Cited By

Maggi FMarrella APatrizi FSkydanienko V(2023)Data-Aware Declarative Process Mining with SATACM Transactions on Intelligent Systems and Technology10.1145/360010614:4(1-26)Online publication date: 10-Aug-2023
https://dl.acm.org/doi/10.1145/3600106
Shen HLiu TZhang Y(2020)Discovery of Learning Path Based on Bayesian Network Association Rule AlgorithmInternational Journal of Distance Education Technologies10.4018/IJDET.202001010418:1(65-82)Online publication date: 1-Oct-2020
https://dl.acm.org/doi/10.4018/IJDET.2020010104
Liu MYang ZJiang JYang K(2020)Correlation Analysis for Complex Equipment Systems Based on Multi-objective Association Rule Mining2020 IEEE 15th International Conference of System of Systems Engineering (SoSE)10.1109/SoSE50414.2020.9130523(337-342)Online publication date: 2-Jun-2020
https://dl.acm.org/doi/10.1109/SoSE50414.2020.9130523

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