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Introduction to Pattern Mining

  • Sebastián Ventura
  • José María Luna
Chapter
  • 1k Downloads

Abstract

This chapter introduces the pattern mining task to the reader, providing formal definitions about patterns, the pattern mining task and the usefulness of patterns in the knowledge discovery process. The utility of the extraction of patterns is introduced by a sample dataset for the market basket analysis. Different type of patterns can be considered from the pattern mining point of view, so an exhaustive taxonomy about patterns in this field is presented, describing concepts such as frequent and infrequent patterns, positive and negative patterns, patterns expressed in compressed forms, sequential patterns, spatio-temporal patterns, etc. Additionally, some pruning strategies to reduce the computational complexity are described, as well as some efficient pattern mining algorithms. Finally, this chapter formally describes how interesting patterns can be associated to analyse the causality by means of association rules.

Keywords

Association Rule Frequent Pattern Pattern Mining Mining Association Rule Mining Frequent Pattern 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    C. C. Aggarwal and J. Han. Frequent Pattern Mining. Springer International Publishing, 2014.CrossRefzbMATHGoogle Scholar
  2. 2.
    C. C. Aggarwal and P. S. Yu. A New Framework For Itemset Generation. In In Proceedings of the 1998 Symposium on Principles of Database Systems, pages 18–24, 1998.Google Scholar
  3. 3.
    C. C. Aggarwal, Y. Li, J. Wang, and J. Wang. Frequent Pattern Mining with Uncertain Data. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’09, pages 29–38, Paris, France, 2009. ACM.Google Scholar
  4. 4.
    R. Agrawal and R. Srikant. Fast Algorithms for Mining Association Rules in Large Databases. In Proceedings of the 20th International Conference on Very Large Data Bases, VLDB ’94, pages 487–499, San Francisco, CA, USA, 1994. Morgan Kaufmann Publishers Inc.Google Scholar
  5. 5.
    R. Agrawal, T. Imielinski, and A. N. Swami. Mining association rules between sets of items in large databases. In Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, SIGMOD Conference ’93, pages 207–216, Washington, DC, USA, 1993.Google Scholar
  6. 6.
    Michael J. Berry and Gordon Linoff. Data Mining Techniques: For Marketing, Sales, and Customer Support. John Wiley & Sons, Inc., New York, NY, USA, 2011.Google Scholar
  7. 7.
    F. Berzal, I. Blanco, D. Sánchez, and M. A. Vila. Measuring the Accuracy and Interest of Association Rules: A new Framework. Intelligent Data Analysis, 6(3):221–235, 2002.zbMATHGoogle Scholar
  8. 8.
    H. Cao, N. Mamoulis, and D. W. Cheung. Mining frequent spatio-temporal sequential patterns. In Proceedings of the 5th IEEE International Conference on Data Mining, ICDM ’05, Houston, Texas, USA, 2005.Google Scholar
  9. 9.
    L. Geng and H. J. Hamilton. Interestingness Measures for Data Mining: A Survey. ACM Computing Surveys, 38, 2006.Google Scholar
  10. 10.
    B. Goethals. Survey on Frequent Pattern Mining. Technical report, Technical report, HIIT Basic Research Unit, Department of Computer Science, University of Helsinki, Finland, 2003.Google Scholar
  11. 11.
    M. Gorawski and P. Jureczek. Extensions for Continuous Pattern Mining. In Proceedings of the 2011 International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2011, pages 194–203, Norwich, UK, 2011.Google Scholar
  12. 12.
    J. Han and M. Kamber. Data Mining: Concepts and Techniques. Morgan Kaufmann, 2000.zbMATHGoogle Scholar
  13. 13.
    J. Han, J. Pei, Y. Yin, and R. Mao. Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach. Data Mining and Knowledge Discovery, 8:53–87, 2004.MathSciNetCrossRefGoogle Scholar
  14. 14.
    J. Han, H. Cheng, D. Xin, and X. Yan. Frequent Pattern Mining: Current Status and Future Directions. Data Mining Knowledge Discovery, 15(1):55–86, 2007.MathSciNetCrossRefGoogle Scholar
  15. 15.
    Y. S. Koh and N. Rountree. Rare Association Rule Mining and Knowledge Discovery: Technologies for Infrequent and Critical Event Detection. Information Science Reference, Hershey, New York, 2010.CrossRefGoogle Scholar
  16. 16.
    Y. Li, A. Algarni, and N. Zhong. Mining Positive and Negative Patterns for Relevance Feature Discovery. In Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’10, pages 753–762, Washington, DC, USA, 2010. ACM.Google Scholar
  17. 17.
    J. M. Luna, J. R. Romero, and S. Ventura. Design and behavior study of a grammar-guided genetic programming algorithm for mining association rules. Knowledge and Information Systems, 32(1):53–76, 2012.CrossRefGoogle Scholar
  18. 18.
    J. M. Luna, J. R. Romero, and S. Ventura. On the adaptability of G3PARM to the extraction of rare association rules. Knowledge and Information Systems, 38(2):391–418, 2014.CrossRefGoogle Scholar
  19. 19.
    J. M. Luna, C. Romero, J. R. Romero, and S. Ventura. An Evolutionary Algorithm for the Discovery of Rare Class Association Rules in Learning Management Systems. Applied Intelligence, 42(3):501–513, 2015.CrossRefGoogle Scholar
  20. 20.
    J. M. Luna, A. Cano, M. Pechenizkiy, and S. Ventura. Speeding-Up Association Rule Mining With Inverted Index Compression. IEEE Transactions on Cybernetics, pp(99):1–14, 2016.Google Scholar
  21. 21.
    N. R. Mabroukeh and C. I. Ezeife. A taxonomy of sequential pattern mining algorithms. ACM Computing Surveys, 43(1):1–41, 2010.CrossRefGoogle Scholar
  22. 22.
    M. Martinez-Ballesteros, I. A. Nepomuceno-Chamorro, and J. C. Riquelme. Inferring gene-gene associations from quantitative association rules. In Proceedings of the 11th International Conference on Intelligent Systems Designe and Applications, ISDA 2011, pages 1241–1246, Cordoba, Spain, 2011.Google Scholar
  23. 23.
    C. H. Mooney and J. F. Roddick. Sequential pattern mining – approaches and algorithms. ACM Computing Surveys, 45(2):1–39, 2013.CrossRefzbMATHGoogle Scholar
  24. 24.
    N. Ordoñez, C. Ezquerra and C. Santana. Constraining and Summarizing Association Rules in Medical Data. Knowledge and Information Systems, 9, 2006.Google Scholar
  25. 25.
    J. Pei and J. Han. Constrained frequent pattern mining: A pattern-growth view. ACM SIGKDD Explorations Newsletter, 4(1):31–39, 2002.CrossRefGoogle Scholar
  26. 26.
    C. Romero and S. Ventura. Educational data mining: a review of the state of the art. IEEE Transactions on Systems, Man, and Cybernetics, Part C, 40(6):601–618, 2010.CrossRefGoogle Scholar
  27. 27.
    C. Romero, J. M. Luna, J. R. Romero, and S. Ventura. Mining Rare Association Rules from e-Learning Data. In Proceedings of the 3rd International Conference on Educational Data Mining, EDM 2010, pages 171–180, Pittsburgh, PA, USA, 2010.Google Scholar
  28. 28.
    D. Sánchez, J. M. Serrano, L. Cerda, and M. A. Vila. Association Rules Applied to Credit Card Fraud Detection. Expert systems with applications, (36):3630–3640, 2008.CrossRefGoogle Scholar
  29. 29.
    A. Savasere, E. Omiecinski, and S. B. Navathe. An efficient algorithm for mining association rules in large databases. In Proceedings of the 21th International Conference on Very Large Data Bases, VLDB ’95, pages 432–444, San Francisco, CA, USA, 1995.Google Scholar
  30. 30.
    T. Scheffer. Finding association rules that trade support optimally against confidence. In Proceedings of the 5th European Conference of Principles and Practice of Knowledge Discovery in Databases, PKDD 2001, pages 424–435, Freiburg, Germany, 2001.Google Scholar
  31. 31.
    M. K. Sohrabi and A. A. Barforoush. Efficient colossal pattern mining in high dimensional datasets. Knowledge-Based Systems, 33:41–52, 2012.CrossRefGoogle Scholar
  32. 32.
    R. Srikant and R. Agrawal. Mining Quantitative Association Rules in Large Relational Tables. In Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data, SIGMOD’96, Montreal, Quebec, Canada, 1996.Google Scholar
  33. 33.
    L. Szathmary, A. Napoli, and P. Valtchev. Towards rare itemset mining. In Proceedings of the 19th IEEE International Conference on Tools with Artificial Intelligence, ICTAI ’07, pages 305–312, Patras, Greece, 2007.Google Scholar
  34. 34.
    L. Szathmary, P. Valtchev, and A. Napoli. Generating Rare Association Rules Using the Minimal Rare Itemsets Family. International Journal of Software and Informatics, 4(3): 219–238, 2010.Google Scholar
  35. 35.
    P. Tan and V. Kumar. Interestingness Measures for Association Patterns: A Perspective. In Proceedings of the Workshop on Postprocessing in Machine Learning and Data Mining, KDD ’00, New York, USA, 2000.Google Scholar
  36. 36.
    P. N. Tan, M. Steinbach, and V. Kumar. Introduction to Data Mining. Addison Wesley, 2005.Google Scholar
  37. 37.
    M. J. Zaki. Scalable algorithms for association mining. IEEE Transactions on Knowledge and Data Engineering, 12(3):372–390, 2000.MathSciNetCrossRefGoogle Scholar
  38. 38.
    C. Zhang and S. Zhang. Association rule mining: models and algorithms. Springer Berlin / Heidelberg, 2002.CrossRefzbMATHGoogle Scholar
  39. 39.
    D. Zhenguo, W. Qinqin, and D. Xianhua. An improved fp-growth algorithm based on compound single linked list. In Proceedings of the 2009 Second International Conference on Information and Computing Science, ICIC ’09, pages 351–353, Washington, DC, USA, 2009. IEEE Computer Society.Google Scholar
  40. 40.
    F. Zhu, X. Yan, J. Han, P. S. Yu, and H. Cheng. Mining colossal frequent patterns by core pattern fusion. In Proceedings of the IEEE 23rd International Conference on Data Engineering, ICDE 2007, pages 706–71, Istanbul, Turkey, 2007. IEEE.Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Sebastián Ventura
    • 1
  • José María Luna
    • 1
  1. 1.Department of Computer Science and Numerical AnalysisUniversity of CordobaCordobaSpain

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