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Pattern Mining with Genetic Algorithms

  • Sebastián Ventura
  • José María Luna
Chapter

Abstract

This chapter describes the use of genetic algorithms for the mining of patterns of interest and the extraction of accurate relationships between them. The current chapter first makes an analysis of the utility of genetic algorithms in the mining of patterns of interest, paying special attention to the computational time and the memory requirements. Then, it describes general issues for any genetic algorithm in the pattern mining field, explaining different ways of representing patterns such as the one used to extract continuous patterns, which include richer information. Additionally, different genetic operators and fitness functions are properly described, denoting their usefulness in the mining of both patterns of interest and accurate associations. Then, different algorithmic approaches in the pattern mining field are analysed. Finally, this chapter deals with a series of application domains in which genetic algorithms for mining either patterns and association rules have been successfully applied.

Keywords

Fitness Function Association Rule Genetic Operator Pattern Mining Continuous Domain 
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.
    M. Affenzeller, S. Winkler, S. Wagner, and A. Beham. Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications. Chapman & Hall/CRC, 1st edition, 2009.Google Scholar
  2. 2.
    C. C. Aggarwal and J. Han. Frequent Pattern Mining. Springer International Publishing, 2014.CrossRefzbMATHGoogle Scholar
  3. 3.
    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
  4. 4.
    B. Alatas and E. Akin. An efficient genetic algorithm for automated mining of both positive and negative quantitative association rules. Soft Computing, 10(3):230–237, 2006.CrossRefGoogle Scholar
  5. 5.
    J. Alcala-Fdez, R. Alcala, M. J. Gacto, and F. Herrera. Learning the membership function contexts for mining fuzzy association rules by using genetic algorithms. Fuzzy Sets and Systems, 160(7):905–921, 2009.MathSciNetCrossRefzbMATHGoogle 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.
    M. Bessaou and P. Siarry. A genetic algorithm with real-value coding to optimize multimodal continuous functions. Structural and Multidisciplinary Optimization, 23(1):63–74, 2002.CrossRefGoogle Scholar
  8. 8.
    M. J. del Jesús, J. A. Gámez, P. González, and J. M. Puerta. On the discovery of association rules by means of evolutionary algorithms. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 1(5):397–415, 2011.Google Scholar
  9. 9.
    M. Delgado, N. Marín, D. Sánchez, and M. A. Vila. Fuzzy association rules: general model and applications. IEEE Transactions on Fuzzy Systems, 11:214–225, 2003.CrossRefGoogle Scholar
  10. 10.
    D. Dubois, H. Prade, and T. Sudkamp. On the representation, measurement, and discovery of fuzzy associations. IEEE Transactions on Fuzzy Systems, 13(2):250–262, 2005.CrossRefGoogle Scholar
  11. 11.
    A. A. Freitas. Data Mining and Knowledge Discovery with Evolutionary Algorithms. Springer-Verlag Berlin Heidelberg, 2002.CrossRefzbMATHGoogle Scholar
  12. 12.
    M. Gendreau and J. Potvin. Handbook of Metaheuristics. Springer Publishing Company, Incorporated, 2nd edition, 2010.CrossRefzbMATHGoogle Scholar
  13. 13.
    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
  14. 14.
    D. E. Goldberg. Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA, 1st edition, 1989.zbMATHGoogle Scholar
  15. 15.
    A. González and F. Herrera. Multi-stage genetic fuzzy systems based on the iterative rule learning approach. Mathware & soft computing, 4(3):233–249, 1997.zbMATHGoogle Scholar
  16. 16.
    F. Herrera. Genetic fuzzy systems: taxonomy, current research trends and prospects. Evolutionary Intelligence, 1(1):27–46, 2008.MathSciNetCrossRefGoogle Scholar
  17. 17.
    J. H. Holland. Adaptation in Natural and Artificial Systems. The University of Michigan Press, 1975.Google Scholar
  18. 18.
    H. Kwasnicka and K. Switalski. Discovery of association rules from medical data: classical and evolutionary approaches. Annales UMCS, Informatica, 4(1):204–217, 2006.Google Scholar
  19. 19.
    Y. Lee, T. Hong, and W. Lin. Mining fuzzy association rules with multiple minimum supports using maximum constraints. In M. Negoita, R. Howlett, and L. Jain, editors, Knowledge-Based Intelligent Information and Engineering Systems, volume 3214 of Lecture Notes in Computer Science, pages 1283–1290. Springer Berlin Heidelberg, 2004.CrossRefGoogle Scholar
  20. 20.
    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
  21. 21.
    M. Lozano, F. Herrera, N. Krasnogor, and D. Molina. Real-coded memetic algorithms with crossover hill-climbing. Evolutionary Computation, pages 3–273, 2004.Google Scholar
  22. 22.
    M. Martinez-Ballesteros, F. Martinez-Alvarez, A. Troncoso, and J. C. Riquelme. Quantitative association rules applied to climatological time series forecasting. In Proceedings of the 10th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2009, pages 284–291, Brugos, Spain, 2009.Google Scholar
  23. 23.
    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 Design and Applications, ISDA 2011, pages 1241–1246, Cordoba, Spain, 2011.Google Scholar
  24. 24.
    M. Martinez-Ballesteros, S. Salcedo-Sanz, J. C. Riquelme, C. Casanova-Mateo, and J. L. Camacho. Evolutionary association rules for total ozone content modeling from satellite observations. Chemometrics and Intelligent Laboratory Systems, 109(2):217–227, 2011.CrossRefGoogle Scholar
  25. 25.
    J. Mata, J. L. Alvarez, and J. C. Riquelme. Mining numeric association rules with genetic algorithms. In Proceedings of the 5th International Conference on Artificial Neural Networks and Genetic Algorithms, ICANNGA 2001, pages 264–267, Taipei, Taiwan, 2001.Google Scholar
  26. 26.
    J. Mata, J. L. Alvarez, and J. C. Riquelme. Discovering numeric association rules via evolutionary algorithm. In Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2002, pages 40–51, Taipei, Taiwan, 2002.Google Scholar
  27. 27.
    M. Mitchell. An Introduction to Genetic Algorithms. MIT Press, Cambridge, MA, USA, 1998.zbMATHGoogle Scholar
  28. 28.
    A. Orriols-Puig, J. Casillas, and F. J. Martínez-López. Unsupervised Learning of Fuzzy Association Rules for Consumer Behavior Modeling. Mathware & soft computing, 16(-):29–43, 2009.Google Scholar
  29. 29.
    H. R. Qodmanan, M. Nasiri, and B. Minaei-Bidgoli. Multi objective association rule mining with genetic algorithm without specifying minimum support and minimum confidence. Expert Systems with Applications, 38:288–298, 2011.CrossRefGoogle Scholar
  30. 30.
    N. S. Rai, S. Jain, and A. Jain. Mining Interesting Positive and Negative Association Rule Based on Improved Genetic Algorithm (MIPNAR_GA). International Journal of Advanced Computer Science and Applications, 5(1), 2014.Google Scholar
  31. 31.
    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
  32. 32.
    A. Salleb-Aouissi, C. Vrain, and C. Nortet. QuantMiner: A Genetic Algorithm for Mining Quantitative Association Rules. In Proceedings of the 20th International Joint Conference on Artificial Intelligence, IJCAI’97, pages 1035–1040, Hyderabad, India, 2007.Google Scholar
  33. 33.
    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
  34. 34.
    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
  35. 35.
    P. D. Shenoy, K. G. Srinivasa, K. R. Venugopal, and L. M. Patnaik. Evolutionary approach for mining association rules on dynamic databases. In Proceedings of the 7th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2003, pages 325–336, Seoul, Korea, 2003.Google Scholar
  36. 36.
    P. D. Shenoy, K. G. Srinivasa, K. R. Venugopal, and L. M. Patnaik. Dynamic association rule mining using genetic algorithms. Intelligent Data Analysis, 9(5):439–453, 2005.Google Scholar
  37. 37.
    E. H. Shortliffe and B. G. Buchanan. A model of inexact reasoning in medicine. Mathematical biosciences, 23:351–379, 1975.MathSciNetCrossRefGoogle Scholar
  38. 38.
    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
  39. 39.
    P. P. Wakabi-Waiswa and V. Baryamureeba. Extraction of interesting association rules using genetic algorithms. International Journal of Computing and ICT Research, 2(1):1818–1828, 2008.Google Scholar
  40. 40.
    X. Yan, C. Zhang, and S. Zhang. Genetic algorithm-based strategy for identifying association rules without specifying actual minimum support. Expert Systems with Applications, 36:3066 – 3076, 2009.Google Scholar
  41. 41.
    C. Zhang and S. Zhang. Association rule mining: models and algorithms. Springer Berlin / Heidelberg, 2002.CrossRefzbMATHGoogle Scholar
  42. 42.
    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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