Constraint-Based Pattern Mining

  • Siegfried NijssenEmail author
  • Albrecht Zimmermann


Many pattern mining systems are designed to solve one specific problem, such as frequent, closed or maximal frequent itemset mining, efficiently. Even though efficient, their specialized nature can make these systems difficult to apply in other situations than the one they were designed for. This chapter provides an overview of generic constraint-based mining systems. Constraint-based pattern mining systems are systems that with minimal effort can be programmed to find different types of patterns satisfying constraints. They achieve this genericity by providing (1) high-level languages in which programmers can easily specify constraints; (2) generic search algorithms that find patterns for any task expressed in the specification language. The development of generic systems requires an understanding of different classes of constraints. This chapter will first provide an overview of such classes constraints, followed by a discussion of search algorithms and specification languages.


Constraints Languages Inductive databases Search algorithms 


  1. 1.
    R. Bayardo. Efficiently mining long patterns from databases. In Proceedings of ACM SIGMOD Conference on Management of Data, 1998.Google Scholar
  2. 2.
    Jérémy Besson, Céline Robardet, and Jean-François Boulicaut. Constraint-based mining of formal concepts in transactional data. In Honghua Dai, Ramakrishnan Srikant, and Chengqi Zhang, editors, Advances in Knowledge Discovery and Data Mining, 8th Pacific-Asia Conference, pages 615–624, Sydney, Australia, May 2004. Springer.Google Scholar
  3. 3.
    Jérémy Besson, Céline Robardet, Jean-François Boulicaut, and Sophie Rome. Constraint-based concept mining and its application to microarray data analysis. Intell. Data Anal., 9(1):59–82, 2005.Google Scholar
  4. 4.
    Hendrik Blockeel, Toon Calders, Élisa Fromont, Bart Goethals, Adriana Prado, and Céline Robardet. An inductive database system based on virtual mining views. Data Min. Knowl. Discov., 24(1), 2012.Google Scholar
  5. 5.
    Francesco Bonchi, Fosca Giannotti, Claudio Lucchese, Salvatore Orlando, Raffaele Perego, and Roberto Trasarti. A constraint-based querying system for exploratory pattern discovery. Inf. Syst., 34(1):3–27, 2009.Google Scholar
  6. 6.
    Francesco Bonchi, Fosca Giannotti, Alessio Mazzanti, and Dino Pedreschi. Examiner: Optimized level-wise frequent pattern mining with monotone constraint. In ICDM, pages 11–18. IEEE Computer Society, 2003.Google Scholar
  7. 7.
    Francesco Bonchi and Bart Goethals. Fp-bonsai: The art of growing and pruning small fp-trees. In PAKDD, pages 155–160, 2004.Google Scholar
  8. 8.
    Cristian Bucila, Johannes Gehrke, Daniel Kifer, and Walker White. DualMiner: A dual-pruning algorithm for itemsets with constraints. In Proceedings of The Eight ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Edmonton, Alberta, Canada, July 23–26 2002.Google Scholar
  9. 9.
    Luc De Raedt. A perspective on inductive databases. SIGKDD Explorations, 4(2):69–77, 2002.CrossRefMathSciNetGoogle Scholar
  10. 10.
    L. De Raedt and S. Kramer. The level wise version space algorithm and its application to molecular fragment finding. In B. Nebel, editor, Proceedings of the 17th International Joint Conference on Artificial Intelligence, pages 853–862. Morgan Kaufmann, 2001.Google Scholar
  11. 11.
    Luc De Raedt, Manfred Jaeger, Sau Dan Lee, and Heikki Mannila. A theory of inductive query answering. In ICDM, pages 123–130. IEEE Computer Society, 2002.Google Scholar
  12. 12.
    Minos N. Garofalakis, Rajeev Rastogi, and Kyuseok Shim. Spirit: Sequential pattern mining with regular expression constraints. In VLDB’99, Proceedings of 25th International Conference on Very Large Data Bases, September 7–10, 1999, Edinburgh, Scotland, UK, pages 223–234. Morgan Kaufmann, 1999.Google Scholar
  13. 13.
    Tias Guns, Anton Dries, Guido Tack, Siegfried Nijssen, and Luc De Raedt. Miningzinc: A modeling language for constraint-based mining. In IJCAI, 2013.Google Scholar
  14. 14.
    Tias Guns, Siegfried Nijssen, and Luc De Raedt. Itemset mining: A constraint programming perspective. Artif. Intell., 175(12–13):1951–1983, 2011.Google Scholar
  15. 15.
    Jiawei Han, Yongjian Fu, Krzystzof Koperski, Wei Wang, and Osmar Zaiane. Dmql: A data mining query language for relational databases. In SIGMOD’96 Workshop. on Research Issues on Data Mining and Knowledge Discovery (DMKD’96), 1996.Google Scholar
  16. 16.
    T. Imielinski and H. Mannila. A database perspectivce on knowledge discovery. Communications of the ACM, 39(11):58–64, 1996.CrossRefGoogle Scholar
  17. 17.
    Daniel Kifer, Johannes Gehrke, Cristian Bucila, and Walker M. White. How to quickly find a witness. In PODS, pages 272–283. ACM, 2003.Google Scholar
  18. 18.
    Stefan Kramer, Luc De Raedt, and Christoph Helma. Molecular feature mining in hiv data. In KDD-2001: The Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2001.Google Scholar
  19. 19.
    Sau Dan Lee and Luc De Raedt. An algebra for inductive query evaluation. In ICDM, pages 147–154, 2003.Google Scholar
  20. 20.
    Heikki Mannila and Hannu Toivonen. Levelwise search and borders of theories in knowledge discovery. Data Mining and Knowledge Discovery, 1(3):241–258, 1997.CrossRefGoogle Scholar
  21. 21.
    Rosa Meo, Giuseppe Psaila, and Stefano Ceri. A new sql-like operator for mining association rules. In VLDB, pages 122–133, 1996.Google Scholar
  22. 22.
    Jean-Philippe Métivier, Patrice Boizumault, Bruno Crémilleux, Mehdi Khiari, and Samir Loudni. A constraint-based language for declarative pattern discovery. In ICDM Workshops, pages 1112–1119, 2011.Google Scholar
  23. 23.
    Raymond T. Ng, Laks V. S. Lakshmanan, Jiawei Han, and Alex Pang. Exploratory mining and pruning optimizations of constrained associations rules. In Proceedings of the ACM-SIGMOD Conference on Management of Data, pages 13–24, 1998.Google Scholar
  24. 24.
    Siegfried Nijssen and Tias Guns. Integrating constraint programming and itemset mining. In ECML/PKDD (2), pages 467–482, 2010.Google Scholar
  25. 25.
    Nicolas Pasquier, Yves Bastide, Rafik Taouil, and Lotfi Lakhal. Discovering frequent closed itemsets for association rules. In Catriel Beeri and Peter Buneman, editors, ICDT, volume 1540 of Lecture Notes in Computer Science, pages 398–416. Springer, 1999.Google Scholar
  26. 26.
    Jian Pei and Jiawei Han. Can we push more constraints into frequent pattern mining? In KDD, pages 350–354, 2000.Google Scholar
  27. 27.
    Jian Pei and Jiawei Han. Constrained frequent pattern mining: a pattern-growth view. SIGKDD Explorations, 4(1):31–39, 2002.CrossRefGoogle Scholar
  28. 28.
    Jian Pei, Jiawei Han, and Laks V. S. Lakshmanan. Pushing convertible constraints in frequent itemset mining. Data Min. Knowl. Discov., 8(3):227–252, 2004.CrossRefMathSciNetGoogle Scholar
  29. 29.
    Arnaud Soulet and Bruno Crémilleux. Mining constraint-based patterns using automatic relaxation. Intell. Data Anal., 13(1):109–133, 2009.Google Scholar
  30. 30.
    Takeaki Uno, Tatsuya Asai, Yuzo Uchida, and Hiroki Arimura. An efficient algorithm for enumerating closed patterns in transaction databases. In Discovery Science, pages 16–31, 2004.Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.KU LeuvenLeuvenBelgium
  2. 2.Universiteit LeidenLeidenThe Netherlands
  3. 3.INSA LyonVilleurbanneFrance

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