{"type":"ScholarlyArticle","title":"Threshold-free Pattern Mining Meets Multi-Objective Optimization: Application to Association Rules","authors":[{"name":"Charles Vernerey","slug":"charles-vernerey","given":"Charles","family":"Vernerey","affiliation":"TASC (LS2N-CNRS) , IMT Atlantique , 44307 Nantes , France"},{"name":"Samir Loudni","slug":"samir-loudni","given":"Samir","family":"Loudni","affiliation":"TASC (LS2N-CNRS) , IMT Atlantique , 44307 Nantes , France"},{"name":"Noureddine Aribi","slug":"noureddine-aribi","given":"Noureddine","family":"Aribi","affiliation":"Université Oran1 , Lab. LITIO , 31000 Oran , Algeria"},{"name":"Yahia Lebbah","slug":"yahia-lebbah","given":"Yahia","family":"Lebbah","affiliation":"Université Oran1 , Lab. LITIO , 31000 Oran , Algeria"}],"abstract":"Constraint-based pattern mining is at the core of numerous data mining tasks. Unfortunately, thresholds which are involved in these constraints cannot be easily chosen. This paper investigates a Multi-objective Optimization approach where several (often conflicting) functions need to be optimized at the same time. We introduce a new model for efficiently mining Pareto optimal patterns with constraint programming. Our model exploits condensed pattern representations to reduce the mining effort. To this end, we design a new  global constraint for ensuring the closeness of patterns over a set of measures. We show how our approach can be applied to derive high-quality non redundant association rules without the use of thresholds whose added-value is studied on both UCI datasets and case study related to the analysis of genes expression data integrating multiple external genes annotations.","doi":"10.24963/ijcai.2022/261","url":"https://ijcai.org/proceedings/2022/261","pdf":"https://ijcai.org/proceedings/2022/0261.pdf","markdown":"https://ijcai.org/proceedings/2022/261.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/261","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Constraint Satisfaction and Optimization","track":"Main Track","number":261,"pages":{"first":1880,"last":1886},"keywords":["Data Mining: Frequent Pattern Mining","Data Mining: Exploratory Data Mining","Constraint Satisfaction and Optimization: Constraint Programming","Constraint Satisfaction and Optimization: Constraint Optimization","Constraint Satisfaction and Optimization: Constraints and Machine Learning"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38987104","length_sec":89,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38985247","length_sec":701,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Agrawal and Srikant, 1994] Rakesh Agrawal and Ramakr- ishnan Srikant. Fast algorithms for mining association rules in large databases. In Proceedings of the 20th VLDB, pages 487-499, San Francisco, CA, USA, 1994.","title":"Rakesh Agrawal and Ramakrishnan Srikant. Fast algorithms for mining association rules in large databases","authors":"Srikant Agrawal","year":"1994","venue":"Proceedings of the 20th VLDB","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Bastide et al., 2000] Yves Bastide, Nicolas Pasquier, Rafik Taouil, Gerd Stumme, and Lofti Lakhal. Mining Minimal Non-redundant Association Rules Using Frequent Closed Itemsets. In Computational Logic -CL 2000, pages 972- 986. Springer, 2000.","title":"Mining Minimal Non-redundant Association Rules Using Frequent Closed Itemsets","authors":"Bastide","year":"2000","venue":"Computational Logic -CL 2000","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 Proceedings of the 2019 SIAM International Conference on Data Mining (SDM), pages 127-135, 2019.","title":"Constraint Programming for Association Rules","authors":"Mohamed-Bachir Belaid, Christian Bessiere, Nadjib Lazaar","year":"2019","venue":"Proceedings of the 2019 SIAM International Conference on Data Mining (SDM)","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Carmona-Saez et al., 2006] Pedro Carmona-Saez, Mon- ica Chagoyen, Andres Rodríguez, Oswaldo Trelles, Jose María Carazo, and Alberto Pascual-Montano. Inte- grated analysis of gene expression by association rules dis- covery. BMC Bioinform., 7:54, 2006.","title":"Pedro Carmona-Saez, Monica Chagoyen, Andres Rodríguez, Oswaldo Trelles, Jose María Carazo, and Alberto Pascual-Montano. Integrated analysis of gene expression by association rules discovery","authors":"Carmona-Saez","year":"2006","venue":"BMC Bioinform","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[De Raedt and Zimmermann, 2007] Luc De Raedt and Al- brecht Zimmermann. Constraint-based pattern set mining. In 7th SIAM SDM, pages 237-248. SIAM, 2007.","title":"Constraint-based pattern set mining","authors":"De Raedt, ; Zimmermann, Albrecht Raedt, Zimmermann","year":"2007","venue":"7th SIAM SDM","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Figueira et al., 2005] José Figueira, Salvatore Greco, and Matthias Ehrgott. Multiple Criteria Decision Analysis: State of the Art Surveys. Springer, 2005.","title":"Multiple Criteria Decision Analysis: State of the Art Surveys","authors":"Figueira","year":"2005","venue":null,"doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Ghosh and Nath, 2004] Ashish Ghosh and Bhabesh Nath. Multi-objective rule mining using genetic algorithms. Inf. Sci., 36(1-3):123-133, 2004.","title":"Multi-objective rule mining using genetic algorithms","authors":"Ashish Ghosh, Bhabesh Nath","year":"2004","venue":"Inf. Sci","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Guns et al., 2011] Tias Guns, Siegfried Nijssen, and Luc De Raedt. Itemset mining: A constraint programming perspective. Artificial Intelligence, 175(12):1951-1983, 2011.","title":"Itemset mining: A constraint programming perspective","authors":"Guns","year":"2011","venue":"Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Hoeve and Katriel, 2006] Willem-Jan van Hoeve and Irit Katriel. Global constraints. In Handbook of Constraint Programming, pages 169-208. Elsevier, 2006.","title":"Handbook of Constraint Programming","authors":"Katriel ; Willem-Jan Hoeve, Irit Van Hoeve, Katriel","year":"2006","venue":null,"doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Izza et al., 2020] Yacine Izza, Said Jabbour, Badran Rad- daoui, and Abdelhamid Boudane. On the enumeration of association rules: A decomposition-based approach. In Proc. of IJCAI 2020, pages 1265-1271, 2020.","title":"On the enumeration of association rules: A decomposition-based approach","authors":"Yacine Izza, Said Jabbour, Badran Raddaoui, Abdelhamid Boudane","year":"2020","venue":"Proc. of IJCAI 2020","doi":null,"url":null,"internal_url":"/proceedings/2020/176"},{"position":11,"raw":"[Kemmar et al., 2017] Amina Kemmar, Yahia Lebbah, Samir Loudni, Patrice Boizumault, and Thierry Charnois. Prefix-projection global constraint and top-k approach for sequential pattern mining. Constraints An Int. J., 22(2):265-306, 2017.","title":"Prefix-projection global constraint and top-k approach for sequential pattern mining","authors":"Amina Kemmar, Yahia Lebbah, Samir Loudni, Patrice Boizumault, Thierry Charnois","year":"2017","venue":"Constraints An Int. J","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Martinez et al., 2007] Ricardo Martinez, Claude Pasquier, and Nicolas Pasquier. Genminer: Mining informative as- sociation rules from genomic data. In Proc. of BIBM 2007, pages 15-22, 2007.","title":"Genminer: Mining informative association rules from genomic data","authors":"Ricardo Martinez, Claude Pasquier, Nicolas Pasquier","year":"2007","venue":"Proc. of BIBM 2007","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Martinez et al., 2008] Ricardo Martinez, Nicolas Pasquier, and Claude Pasquier. GenMiner: mining non-redundant association rules from integrated gene expression data and annotations. Bioinformatics, 24(22):2643-2644, 2008.","title":"GenMiner: mining non-redundant association rules from integrated gene expression data and annotations","authors":"Ricardo Martinez, Nicolas Pasquier, Claude Pasquier","year":"2008","venue":"Bioinformatics","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Négrevergne et al., 2013] Benjamin Négrevergne, Anton Dries, Tias Guns, and Siegfried Nijssen. Dominance pro- gramming for itemset mining. In Proceedings of the 13th ICDM, pages 557-566. IEEE Computer Society, 2013.","title":"Dominance programming for itemset mining","authors":"Négrevergne","year":"2013","venue":"Proceedings of the 13th ICDM","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Omiecinski, 2003] Edward Robert Omiecinski. Alternative interest measures for mining associations in databases. IEEE TKDE, 15(1):57-69, 2003.","title":"Alternative interest measures for mining associations in databases","authors":"Edward Robert Omiecinski, Omiecinski","year":"2003","venue":"IEEE TKDE","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Pasquier et al., 1999] Nicolas Pasquier, Yves Bastide, Rafik Taouil, and Lofti Lakhal. Discovering frequent closed itemsets for association rules. In Proceedings of the 7th ICDT, pages 398-416, 1999.","title":"Discovering frequent closed itemsets for association rules","authors":"Nicolas Pasquier, Yves Bastide, Rafik Taouil, Lofti Lakhal","year":"1999","venue":"Proceedings of the 7th ICDT","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Prud'homme et al., 2016] Charles Prud'homme, Jean- Guillaume Fages, and Xavier Lorca. Choco Solver Documentation, 2016.","title":"Choco Solver Documentation","authors":"Prud'homme","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Schaus and Hartert, 2013] Pierre Schaus and Renaud Har- tert. Multi-Objective Large Neighborhood Search. In Pro- ceedings of CP 2013, 2013.","title":"Multi-Objective Large Neighborhood Search","authors":"Hartert Schaus, Pierre Schaus, Renaud Hartert","year":"2013","venue":"Proceedings of CP 2013","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Schaus et al., 2017] Pierre Schaus, John Oscar Raoul Aoga, and Tias Guns. Coversize: A global constraint for frequency-based itemset mining. In Proceedings of the 23rd CP 2017, pages 529-546, 2017.","title":"Coversize: A global constraint for frequency-based itemset mining","authors":"Schaus","year":"2017","venue":"Proceedings of the 23rd CP 2017","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Soulet and Crémilleux, 2008] Arnaud Soulet and Bruno Crémilleux. Adequate condensed representations of pat- terns. Data Min. Knowl. Discov., 17(1):94-110, 2008.","title":"Adequate condensed representations of patterns","authors":"Crémilleux Soulet","year":"2008","venue":"Data Min. Knowl. Discov","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Soulet et al., 2011] Arnaud Soulet, Chedy Raïssi, Marc Plantevit, and Bruno Crémilleux. Mining dominant pat- terns in the sky. In Proceedings of the ICDM 2011, pages 655-664. IEEE Computer Society, 2011.","title":"Mining dominant patterns in the sky","authors":"Soulet","year":"2011","venue":"Proceedings of the ICDM 2011","doi":null,"url":null,"internal_url":null},{"position":22,"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 Proc. of CLA '08, pages 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":"Proc. of CLA '08","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Ugarte et al., 2017] Willy Ugarte, Patrice Boizumault, Bruno Crémilleux, Alban Lepailleur, Samir Loudni, Marc Plantevit, Chedy Raïssi, and Arnaud Soulet. Skypattern mining: From pattern condensed representations to dynamic constraint satisfaction problems. Artif. Intell., 244:48-69, 2017.","title":"Skypattern mining: From pattern condensed representations to dynamic constraint satisfaction problems","authors":"Willy Ugarte, Patrice Boizumault, Bruno Crémilleux, Alban Lepailleur, Samir Loudni, Marc Plantevit, Chedy Raïssi, Arnaud Soulet","year":"2017","venue":"Artif. Intell","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[van Leeuwen and Ukkonen, 2013] Matthijs van Leeuwen and Antti Ukkonen. Discovering skylines of subgroup sets. In ECML PKDD 2013, pages 272-287, 2013.","title":"Discovering skylines of subgroup sets","authors":"Ukkonen ; Matthijs Van Leeuwen, Antti Van Leeuwen, Ukkonen","year":"2013","venue":"ECML PKDD 2013","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Vernerey et al., 2022] Charles Vernerey, Samir Loudni, Yahia Lebbah, and Noureddine Aribi. Code and supple- mentary material. https://gitlab.com/chaver/data-mining, 2022. Accessed: 2022-05-16.","title":"Code and supplementary material","authors":"Charles Vernerey, Samir Loudni, Yahia Lebbah, Noureddine Aribi","year":"2022","venue":null,"doi":null,"url":"https://gitlab.com/chaver/data-mining,2022","internal_url":null},{"position":26,"raw":"[Wang et al., 2005] Jianyong Wang, Jiawei Han, Ying Lu, and Petre Tzvetkov. TFP: an efficient algorithm for mining top-k frequent closed itemsets. IEEE Trans. Knowl. Data Eng., 17(5):652-664, 2005.","title":"TFP: an efficient algorithm for mining top-k frequent closed itemsets","authors":"Jianyong Wang, Jiawei Han, Ying Lu, Petre Tzvetkov","year":"2005","venue":"IEEE Trans. Knowl. Data Eng","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}