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Progress in Artificial Intelligence

, Volume 5, Issue 3, pp 165–170 | Cite as

Pattern mining: current status and emerging topics

  • Jose Maria LunaEmail author
Regular Paper

Abstract

The extraction of patterns of interest and associations between them have been a major research topic since its definition at the beginning of the nineties. Abundant research studies have been dedicated to this field, providing overwhelming progresses in both efficiency and scalability, and extracting patterns from different data structures and domains. Since pattern mining is the keystone of data analysis, many application fields and, specially, numerous researchers have focused their attention on the discovery of patterns and associations that describe and represent any type of homogeneity and regularity in data. The growing scope of applications of pattern mining has deep impact on pattern mining models based on data domains, data dimensionality, data comprehensibility and data flexibility. All of these provides new and challenging research issues that need to be solved, broaden new research lines and leaving early pattern mining problems that can be considered as solved already.

Keywords

Pattern mining Data comprehensibility Data dimensionality Data flexibility 

Notes

Acknowledgments

This work was supported by the Spanish Ministry of Economy and Competitiveness under the Project TIN2014-55252-P, and FEDER funds.

References

  1. 1.
    Abadi, D.J., Madden, S., Ferreira, M.: Integrating compression and execution in column-oriented database systems. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 671–682, SIGMOD Conference, Chicago, Illinois, USA (2006)Google Scholar
  2. 2.
    Adhikary, D., Roy, S.: Trends in quantitative association rule mining techniques. In: Proceedings of the 2nd IEEE International Conference on Recent Trends in Information Systems. ReTIS 2015. pp. 126–131, Kolkata, India, July 9–11 (2015)Google Scholar
  3. 3.
    Aggarwal, C.C., Han, J.: Frequent Pattern Mining. Springer, Switzerland (2014). doi: 10.1007/978-3-319-07821-2_2 CrossRefzbMATHGoogle Scholar
  4. 4.
    Aggarwal, C.C., Yu, P.S.: A New Framework For Itemset Generation. In: Proceedings of the 1998 Symposium on Principles of Database Systems, pp. 18–24 (1998)Google Scholar
  5. 5.
    Agrawal, R., Imielinski, T., Swami, A.N.: 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, pp. 207–216, Washington, DC, USA (1993)Google Scholar
  6. 6.
    Alatas, B., Akin, E.: An efficient genetic algorithm for automated mining of both positive and negative quantitative association rules. Soft Comput. 10(3), 230–237 (2006)CrossRefGoogle Scholar
  7. 7.
    Alcala-Fdez, J., Alcala, R., Gacto, M.J., Herrera, F.: Learning the membership function contexts for mining fuzzy association rules by using genetic algorithms. Fuzzy Sets Syst. 160(7), 905–921 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Brézillon, P.: Context in problem solving: a survey. Knowl. Eng. Rev. 14(01), 47–80 (1999)CrossRefzbMATHGoogle Scholar
  9. 9.
    Brin, S., Motwani, R., Ullman, J.D., Tsur, S.: Dynamic Itemset Counting and Implication Rules for Market Basket Data. In: Proceedings of the 1997 ACM SIGMOD International Conference on Management of Data. SIGMOD ’97, pp. 255–264, Tucson, Arizona, USA, ACM (1997)Google Scholar
  10. 10.
    Cano, A., Luna, J.M., Ventura, S.: High performance evaluation of evolutionary-mined association rules on gpus. J. Supercomput. 66(3), 1438–1461 (2013)CrossRefGoogle Scholar
  11. 11.
    Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. Commun. ACM 51(1), 107–113 (2008)CrossRefGoogle Scholar
  12. 12.
    del Jesús, M.J., Gámez, J.A., González, P., Puerta, J.M.: On the discovery of association rules by means of evolutionary algorithms. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 1(5), 397–415 (2011)CrossRefGoogle Scholar
  13. 13.
    Freitas, A.A.: Data Mining and Knowledge Discovery with Evolutionary Algorithms. Springer-Verlag, Berlin (2002)CrossRefzbMATHGoogle Scholar
  14. 14.
    Ghemawat, S., Gobioff, H., Leung, S.-T.: The google file system. Oper. Syst. Rev. (ACM) 37(5), 29–43 (2003)CrossRefGoogle Scholar
  15. 15.
    Goethals, B., Le Page, W., Mampaey, M.: Mining interesting sets and rules in relational databases. In: Proceedings of the ACM Symposium on Applied Computing, pp. 997–1001, Sierre, Switzerland (2010)Google Scholar
  16. 16.
    Goethals, B., Moens, S., Vreeken, J.: MIME: A Framework for Interactive Visual Pattern Mining. In: Gunopulos, D., Hofmann, T., Malerba, D., Vazirgiannis, M. (eds.) Machine Learning and Knowledge Discovery in Databases, volume 6913 of Lecture Notes in Computer Science, pp. 634–637. Springer, Berlin (2011)Google Scholar
  17. 17.
    Goethals, B., Zaki, M.J.: Advances in frequent itemset mining implementations: report on fimi’03. ACM SIGKDD Explor. Newsl. 6(1), 109–117 (2004)CrossRefGoogle Scholar
  18. 18.
    Gorawski, M., Jureczek, P.: Extensions for Continuous Pattern Mining. In: Proceedings of the 2011 International Conference on Intelligent Data Engineering and Automated Learning. IDEAL 2011, pp. 194–203. Norwich, UK (2011)Google Scholar
  19. 19.
    Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann, Waltham (2000)zbMATHGoogle Scholar
  20. 20.
    Koh, Y.S., Rountree, N.: Rare Association Rule Mining and Knowledge Discovery: Technologies for Infrequent and Critical Event Detection. Information Science Reference, Hershey, New York (2010)CrossRefGoogle Scholar
  21. 21.
    Leman, D., Feelders, A., Knobbe, A.J.: Exceptional model mining. In: Proceedings of the European Conference in Machine Learning and Knowledge Discovery in Databases, volume 5212 of ECML/PKDD 2008, pp. 1–16, Antwerp, Springer, Belgium (2008)Google Scholar
  22. 22.
    Liu, B., Hsu, W., Chen, S., Ma, Y.: Analyzing the subjective interestingness of association rules. IEEE Intell. Syst. 15(5), 47–55 (2000)CrossRefGoogle Scholar
  23. 23.
    Luk, R.W.P., Lam, W.: Efficient in-memory extensible inverted file. Inf. Syst. 32(5), 733–754 (2007)CrossRefGoogle Scholar
  24. 24.
    Luna, J.M., Cano, A., Pechenizkiy, M., Ventura, S.: Speeding-up association rule mining with inverted index compression. IEEE Trans. Cybern. (2016). doi: 10.1109/TCYB.2015.2496175
  25. 25.
    Luna, J.M., Cano, A., Ventura, S.: Genetic programming for mining association rules in relational database environments. In: Gandomi, A.H., Alavi, A.H., Ryan, C. (eds.) Handbook of Genetic Programming Applications, pp. 431–450. Springer, Berlin (2015)CrossRefGoogle Scholar
  26. 26.
    Luna, J.M., Pechenizkiy, M., Ventura, S.: Mining exceptional relationships with grammar-guided genetic programming. Knowl. Inf. Syst. (2016). doi: 10.1007/s10115-015-0859-y
  27. 27.
    Luna, J.M., Romero, C., Romero, J.R., Ventura, S.: An evolutionary algorithm for the discovery of rare class association rules in learning management systems. Appl. Intell. 42(3), 501–513 (2015)CrossRefGoogle Scholar
  28. 28.
    Luna, J.M., Romero, J.R., Romero, C., Ventura, S.: Reducing gaps in quantitative association rules: a genetic programming free-parameter algorithm. Integr. Comput. Aided Eng. 21(4), 321–337 (2014)Google Scholar
  29. 29.
    Luna, J.M., Romero, J.R., Ventura, S.: Design and behavior study of a grammar-guided genetic programming algorithm for mining association rules. Knowl. Inf. Syst. 32(1), 53–76 (2012)CrossRefGoogle Scholar
  30. 30.
    Luna, J.M., Romero, J.R., Romero, C., Ventura, S.: On the use of genetic programming for mining comprehensible rules in subgroup discovery. IEEE Trans. Cybern. 44(12), 2329–2341 (2014)CrossRefGoogle Scholar
  31. 31.
    Martín, D., Rosete, A., Alcalá, J., Herrera, F.: A new multiobjective evolutionary algorithm for mining a reduced set of interesting positive and negative quantitative association rules. IEEE Trans. Evol. Comput. 18(1), 54–69 (2014)CrossRefGoogle Scholar
  32. 32.
    Martín, D., Rosete, A., Alcalá-Fdez, J., Herrera, F.: Qar-cip-nsga-ii: a new multi-objective evolutionary algorithm to mine quantitative association rules. Inf. Sci. 258, 1–28 (2014)MathSciNetCrossRefGoogle Scholar
  33. 33.
    Martínez-Ballesteros, M., Nepomuceno-Chamorro, I.A., Riquelme, J.C.: Discovering gene association networks by multi-objective evolutionary quantitative association rules. J. Comput. Syst. Sci. 80(1), 118–136 (2014)MathSciNetCrossRefzbMATHGoogle Scholar
  34. 34.
    Moens, S., Aksehirli, E., Goethals, B.: Frequent itemset mining forbig data. In: Proceedings of the 2013 IEEE International Conferenceon Big Data, pp.111–118, Santa Clara, CA, USA (2013)Google Scholar
  35. 35.
    Ordoñez, N., Ezquerra, C., Santana, C.: Constraining and summarizing association rules in medical data. Knowl. Inf. Syst. 9(3), 259–283 (2006)Google Scholar
  36. 36.
    Srikant, R. Agrawal, R.: 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
  37. 37.
    Yan, X., Zhang, C., Zhang, S.: Genetic algorithm-based strategy for identifying association rules without specifying actual minimum support. Expert Syst. Appl. 36, 3066–3076 (2009)CrossRefGoogle Scholar
  38. 38.
    Zhang, C., Zhang, S.: Association Rule Mining: Models and Algorithms. Springer, Berlin (2002)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Department of Computer Science and Numerical AnalysisUniversity of CordobaCordobaSpain

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