{"type":"ScholarlyArticle","title":"On the Enumeration of Association Rules: A Decomposition-based Approach","authors":[{"name":"Yacine Izza","slug":"yacine-izza","given":"Yacine","family":"Izza","affiliation":"ANITI , Toulouse , France; CRIL & CNRS , Université d'Artois , Lens , France"},{"name":"Said Jabbour","slug":"said-jabbour","given":"Said","family":"Jabbour","affiliation":""},{"name":"Badran Raddaoui","slug":"badran-raddaoui","given":"Badran","family":"Raddaoui","affiliation":"SAMOVAR, Télécom SudParis , Institut Polytechnique de Paris , France"},{"name":"Abdelahmid Boudane","slug":"abdelahmid-boudane","given":"Abdelahmid","family":"Boudane","affiliation":"LMTO , Ecole Militaire Polytechnique , Algeria"}],"abstract":"While traditional data mining techniques have been used extensively for finding patterns in databases, they are not always suitable for incorporating user-specified constraints. To overcome this issue, CP and SAT based  frameworks for modeling and solving pattern mining tasks have gained a considerable audience in recent years. However, a bottleneck for all these CP and SAT-based approaches is the encoding size which makes these algorithms inefficient for large databases. This paper introduces a practical SAT-based approach to discover efficiently (minimal non-redundant) association rules. First, we present a decomposition-based paradigm that splits the original transaction database into smaller and independent subsets. Then, we show that without producing too large formulas, our decomposition method allows independent mining evaluation on a multi-core machine,  improving  performance. Finally, an experimental evaluation shows that our method is fast  and scale  well  compared  with  the existing CP approach even in the sequential case, while significantly reducing the gap with the best state-of-the-art specialized algorithm.","doi":"10.24963/ijcai.2020/176","url":"https://ijcai.org/proceedings/2020/176","pdf":"https://ijcai.org/proceedings/2020/0176.pdf","markdown":"https://ijcai.org/proceedings/2020/176.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/176","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Data Mining","track":"Main track","number":176,"pages":{"first":1265,"last":1271},"keywords":["Constraints and SAT: Constraints and Data Mining ; Constraints and Machine Learning","Constraints and SAT: Constraints: Modeling, Solvers, Applications","Data Mining: Frequent Pattern Mining"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/25100/file_25100.m3u8","length_sec":287,"provider":"hls"}],"references":[{"position":1,"raw":"[Agrawal and Srikant, 1994] Rakesh Agrawal and Ramakr- ishnan Srikant. Fast algorithms for mining association rules in large databases. In VLDB, pages 487-499, 1994.","title":"Rakesh Agrawal and Ramakrishnan Srikant. Fast algorithms for mining association rules in large databases","authors":"Srikant Agrawal","year":"1994","venue":"VLDB","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Bastide et al., 2000] Yves Bastide, Nicolas Pasquier, Rafik Taouil, Gerd Stumme, and Lotfi Lakhal. Mining mini- mal non-redundant association rules using frequent closed itemsets. In Computational Logic, volume 1861, pages 972-986, 2000.","title":"Mining minimal non-redundant association rules using frequent closed itemsets","authors":"Bastide","year":"2000","venue":"Computational Logic","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Belaid et al., 2019] Mohamed-Bachir Belaid, Christian Bessiere, and Nadjib Lazaar. Constraint programming for association rules. In SDM, pages 127-135, 2019.","title":"Constraint programming for association rules","authors":"Mohamed-Bachir Belaid, Christian Bessiere, Nadjib Lazaar","year":"2019","venue":"SDM","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Boudane et al., 2016] Abdelhamid Boudane, Saïd Jabbour, Lakhdar Sais, and Yakoub Salhi. A SAT-based approach for mining association rules. In IJCAI, pages 2472-2478, 2016.","title":"A SAT-based approach for mining association rules","authors":"Abdelhamid Boudane, Saïd Jabbour, Lakhdar Sais, Yakoub Salhi","year":"2016","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2016/352"},{"position":5,"raw":"[Boudane et al., 2017a] Abdelhamid Boudane, Saïd Jab- bour, Lakhdar Sais, and Yakoub Salhi. Clustering complex data represented as propositional formulas. In PAKDD, pages 441-452, 2017.","title":"Clustering complex data represented as propositional formulas","authors":"Boudane","year":"2017","venue":"PAKDD","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Boudane et al., 2017b] Abdelhamid Boudane, Saïd Jab- bour, Lakhdar Sais, and Yakoub Salhi. Enumerating non- redundant association rules using satisfiability. In PAKDD, pages 824-836, 2017.","title":"Enumerating nonredundant association rules using satisfiability","authors":"Boudane","year":"2017","venue":"PAKDD","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Ceglar and Roddick, 2006] Aaron Ceglar and John F. Rod- dick. Association mining. ACM Comput. Surv., 38(2):5, 2006.","title":"Association mining","authors":"Aaron Ceglar, John F","year":"2006","venue":"ACM Comput. Surv","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Dao et al., 2017] Thi-Bich-Hanh Dao, Khanh-Chuong Duong, and Christel Vrain. Constrained clustering by constraint programming. Artif. Intell., 244:70-94, 2017.","title":"Constrained clustering by constraint programming","authors":"Thi-Bich-Hanh Dao, Khanh-Chuong Duong, Christel Vrain","year":"2017","venue":"Artif. Intell","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Davis et al., 1962] Martin Davis, George Logemann, and Donald Loveland. A machine program for theorem prov- ing. Communications of the ACM, 5:394-397, 1962. [Eén and Sörensson, 2002] Niklas Eén and Niklas Sörensson. An extensible sat-solver. In SAT, pages 502-518, 2002.","title":"A machine program for theorem proving","authors":"Martin Davis, George Logemann, Donald Loveland","year":"1962","venue":"Communications of the ACM","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Ganji et al., 2017] Mohadeseh Ganji, James Bailey, and Pe- ter J. Stuckey. A declarative approach to constrained com- munity detection. In CP, pages 477-494, 2017.","title":"A declarative approach to constrained community detection","authors":"Ganji","year":"2017","venue":"CP","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Gebser et al., 2016] Martin Gebser, Thomas Guyet, René Quiniou, Javier Romero, and Torsten Schaub. Knowledge- based sequence mining with ASP. In IJCAI, pages 1497- 1504, 2016.","title":"Knowledgebased sequence mining with ASP","authors":"Martin Gebser, Thomas Guyet, René Quiniou, Javier Romero, Torsten Schaub","year":"2016","venue":"IJCAI","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Han et al., 2004] Jiawei Han, Jian Pei, Yiwen Yin, and Run- ying Mao. Mining frequent patterns without candidate generation: A frequent-pattern tree approach. Data Min. Knowl. Discov., 8(1):53-87, 2004.","title":"Mining frequent patterns without candidate generation: A frequent-pattern tree approach","authors":"Jiawei Han, Jian Pei, Yiwen Yin, Runying Mao","year":"2004","venue":"Data Min. Knowl. Discov","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Henriques et al., 2012] Rui Henriques, Inês Lynce, and Vasco M. Manquinho. On when and how to use SAT to mine frequent itemsets. CoRR, abs/1207.6253, 2012.","title":"On when and how to use SAT to mine frequent itemsets","authors":"Rui Henriques, Inês Lynce, Vasco M Manquinho","year":"2012","venue":null,"doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Jabbour et al., 2013] Saïd Jabbour, Lakhdar Sais, and Yak- oub Salhi. The top-k frequent closed itemset mining using top-k sat problem. In ECML/PKDD, pages 403-418, 2013.","title":"The top-k frequent closed itemset mining using top-k sat problem","authors":"Saïd Jabbour, Lakhdar Sais, Yakoub Salhi","year":"2013","venue":"ECML/PKDD","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Jabbour et al., 2017] Saïd Jabbour, Nizar Mhadhbi, Badran Raddaoui, and Lakhdar Sais. A SAT-based framework for overlapping community detection in networks. In PAKDD, pages 786-798, 2017.","title":"A SAT-based framework for overlapping community detection in networks","authors":"Jabbour","year":"2017","venue":"PAKDD","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Jabbour et al., 2020] Said Jabbour, Nizar Mhadhbi, Badran Raddaoui, and Lakhdar Sais. SAT-based models for over- lapping community detection in networks. Computing, 102(5):1275-1299, 2020.","title":"SAT-based models for overlapping community detection in networks","authors":"Said Jabbour, Nizar Mhadhbi, Badran Raddaoui, Lakhdar Sais","year":"2020","venue":"Computing","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Kryszkiewicz, 1998] Marzena Kryszkiewicz. Representa- tive association rules and minimum condition maximum consequence association rules. In PKDD, pages 361-369, 1998.","title":"Marzena Kryszkiewicz. Representative association rules and minimum condition maximum consequence association rules","authors":"Kryszkiewicz","year":"1998","venue":"PKDD","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Lazaar et al., 2016] Nadjib Lazaar, Yahia Lebbah, Samir Loudni, Mehdi Maamar, Valentin Lemière, Christian Bessiere, and Patrice Boizumault. A global constraint for closed frequent pattern mining. In CP, pages 333-349, 2016.","title":"A global constraint for closed frequent pattern mining","authors":"Lazaar","year":"2016","venue":"CP","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Négrevergne and Guns, 2015] Benjamin Négrevergne and Tias Guns. Constraint-based sequence mining using con- straint programming. In CPAIOR, pages 288-305, 2015.","title":"Benjamin Négrevergne and Tias Guns. Constraint-based sequence mining using constraint programming","authors":"Guns Négrevergne","year":"2015","venue":"CPAIOR","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Schaus et al., 2017] Pierre Schaus, John O. R. Aoga, and Tias Guns. Coversize: A global constraint for frequency- based itemset mining. In CP, pages 529-546, 2017.","title":"Coversize: A global constraint for frequencybased itemset mining","authors":"Pierre Schaus, O R John, Tias Aoga, Guns","year":"2017","venue":"CP","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Szathmary et al., 2008] Laszlo Szathmary, Petko Valtchev, Amedeo Napoli, and Robert Godin. An efficient hybrid algorithm for mining frequent closures and generators. In CLA, page 47-58, 2008.","title":"An efficient hybrid algorithm for mining frequent closures and generators","authors":"Laszlo Szathmary, Petko Valtchev, Amedeo Napoli, Robert Godin","year":"2008","venue":"CLA","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Taouil et al., 2000] Rafik Taouil, Nicolas Pasquier, Yves Bastide, and Lotfi Lakhal. Mining bases for association rules using closed sets. In Proceedings of ICDE, page 307, 2000.","title":"Mining bases for association rules using closed sets","authors":"Taouil","year":"2000","venue":"Proceedings of ICDE","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}