Advertisement

Soft Set and Genetic Algorithms for Association Rule Mining: A Road Map and Direction for Hybridization

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 199)

Abstract

Association rules have relied on user-specified threshold of support and confidence. With no prior/little domain knowledge, if the user is specifying threshold for the mining task; then there is a direct impact on quality of association rules. In this paper, we have discussed some of the early attempts of choosing automatically the user specified threshold (i.e., no user intervention to specify threshold) by soft set and genetic algorithms for association rule mining.

The reason of being restricted with soft set and genetic algorithms is that: association rule using soft set is free from inadequacy of the parameterization tools, which can also deals with uncertainty. Alongside, genetic algorithms can help to user for finding out optimal threshold for generating a number of interesting and novel association rules. Furthermore, we discuss the possibility of hybridization and their future usage in association rule mining.

Keywords

Soft set Genetic Algorithm Rule mining 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Borgelt, C.: Association Rule Induction (2005), http://fuzzy.cs.uni-magdeburg.de/~borgelt
  2. 2.
    Kuok, C., Fu, A., Wong, M.: Mining fuzzy association rules in databases. SIGMOD Record 27(1), 41–46 (1998)CrossRefGoogle Scholar
  3. 3.
    Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proc. 1993 ACMSIGMOD Int. Conf. Management Data, Washington, DC, pp. 207–216 (1993)Google Scholar
  4. 4.
    Maeda, A., Ashida, H., Taniguchi, Y., Takahashi, Y.: Data mining system using fuzzy rule induction. In: Proc. IEEE Int. Conf. Fuzzy Syst. FUZZ IEEE 1995, pp. 45–46 (March 1995)Google Scholar
  5. 5.
    Wei, Q., Chen, G.: Mining generalized association rules with fuzzy taxonomic structures. In: Proc. NAFIPS 1999, New York, pp. 477–481 (June 1999)Google Scholar
  6. 6.
    Au, W.H., Chan, K.C.C.: An effective algorithm for discovering fuzzy rules in relational databases. In: Proc. IEEE Int. Conf. Fuzzy Syst. FUZZ IEEE 1998, pp. 1314–1319 (May 1998)Google Scholar
  7. 7.
    Flockhart, I.W., Radcliffe, N.J.: A genetic algorithm-based approach to data mining. In: Proc. 2nd Int. Conf. Knowledge Discovery Data Mining (KDD 1996), Portland, OR, August 2-4, p. 299 (1996)Google Scholar
  8. 8.
    Raymer, M.L., Punch, W.F., Goodman, E.D., Kuhn, L.A.: Genetic programming for improved data mining: An application to the biochemistry of protein interactions. In: Proc. 1st Annu. Conf. Genetic Programming 1996, Stanford Univ., CA, July 28-31, pp. 375–380 (1996)Google Scholar
  9. 9.
    Ryu, T., Eick, C.F.: MASSON: Discovering commonalties in collection of objects using genetic programming. In: Proc. 1st Annu. Conf. Genetic Programming 1996, Stanford Univ., CA, July 28-31, pp. 200–208 (1996)Google Scholar
  10. 10.
    Teller, A., Veloso, M.: Program evolution for data mining. Int. J. Expert Syst. 8, 216–236 (1995)Google Scholar
  11. 11.
    Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R. (eds.): Advances in Knowledge Discovery and Data Mining. AAAI/MIT Press, Menlo Park, CA (1996)Google Scholar
  12. 12.
    Xu, K., Wang, Z., Leung, K.S.: Using a new type of nonlinear integral for multiregression: An application of evolutionary algorithms in data mining. In: Proc. IEEE Int. Conf. Syst., Man, Cybern., pp. 2326–2331 (October 1998)Google Scholar
  13. 13.
    Noda, E., Freitas, A.A., Lopes, H.S.: Discovering interesting prediction rules with a genetic algorithm. In: Proc. IEEE Congr. Evolutionary Comput., CEC 1999, pp. 1322–1329 (July 1999)Google Scholar
  14. 14.
    Cheung, Y., Fu, A.: Mining frequent itemsets without support threshold: With and without item constraints. IEEE Transactions on Knowledge and Data Engineering (2004)Google Scholar
  15. 15.
    Zhang, S., Lu, J., Zhang, C.: A fuzzy-logic-based method to acquire user threshold of minimum-support for mining association rules. Information Sciences (2004)Google Scholar
  16. 16.
    Molodtsov, D.: Soft set theory-first results. Computers and Mathematics with Applications 37, 19–31 (1999)CrossRefMATHMathSciNetGoogle Scholar
  17. 17.
    Feldman, R., Aumann, Y., Amir, A., Zilberstein, A., Klosgen, W.: Maximal association rules: a new tool for mining for keywords co-occurrences in document collections. In: The Proceedings of the KDD, pp. 167–170 (1999)Google Scholar
  18. 18.
    Amir, A., Aumann, Y., Feldman, R., Fresco, M.: Maximal association rules: a tool for mining associations in text. Journal of Intelligent Information Systems 25(3), 333–345 (2005)CrossRefGoogle Scholar
  19. 19.
    Guan, J.W., Bell, D.A., Liu, D.Y.: The rough set approach to association rule mining. In: The Proceedings of the Third IEEE International Conference on Data Mining (ICDM 2003), pp. 529–532 (2005)Google Scholar
  20. 20.
    Freitas, A.: A genetic algorithm for generalized rule induction. In: Engineering Design and Manufacturing. AISC, pp. 340–353. Springer, Berlin (1999)Google Scholar
  21. 21.
    Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, Dallas, TX, USA, pp. 1–12 (2000)Google Scholar
  22. 22.
    Park, J., Chen, M., Yu, P.: Using a hash–based method with transaction trimming for mining association rules. IEEE Trans., Knowledge and Data Eng. 9(5), 813–824 (1997)CrossRefGoogle Scholar
  23. 23.
    Aggarawal, C., Yu, P.: A new framework for itemset generation. In: Proceedings of the PODS Conference, Seattle, WA, USA, pp. 18–24 (June 1998)Google Scholar
  24. 24.
    Dubois, D., Prade, H.: Rough fuzzy sets and fuzzy rough sets. International Journal of General Systems 17, 191–208 (1990)CrossRefMATHGoogle Scholar
  25. 25.
    Bi, Y., Anderson, T., McClean, S.: A rough set model with ontologies for discovering maximal association rules in document collections. Knowledge-Based Systems 16, 243–251 (2003)CrossRefGoogle Scholar
  26. 26.
    Aggarawal, C., Yu, P.: A new framework for itemset generation. In: Proceedings of the PODS Conference, Seattle, WA, USA, pp. 18–24 (June 1998)Google Scholar
  27. 27.
    Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, Washington, D.C., pp. 207–216 (May 1993)Google Scholar
  28. 28.
    Au, W., Chan, C.: An evolutionary approach for discovering changing patterns in historical data. In: Proceedings of SPIE, pp. 398–409 (2002)Google Scholar
  29. 29.
    Silverstein, C., Brin, S., Motwani, R.: Beyond market baskets: Generalizing association rules to dependence rules. In: Data Mining and Knowledge Discovery, pp. 39–68 (1998)Google Scholar
  30. 30.
    Toivonen, H.: Sampling large databases for association rules. In: Proceedings of the 22nd VLDB Conference, pp. 134–145 (1996)Google Scholar
  31. 31.
    Webb, G.: Efficient search for association rules. In: Proceedings of ACM SIGKDD, New York, pp. 99–107 (2000)Google Scholar
  32. 32.
    Park, J., Chen, M., Yu, P.: Using a hash–based method with transaction trimming for mining association rules. IEEE Trans. Knowledge and Data Eng. 9(5), 813–824 (1997)CrossRefGoogle Scholar
  33. 33.
    Zhang, C., Zhang, S., Webb, G.: Identifying approximate itemsets of interest in large databases. Applied Intelligence 18, 91–104 (2003)CrossRefGoogle Scholar
  34. 34.
    Piatetsky-Shapiro, G.: Discovery, analysis and presentation of strong rules. In: Piatetsky-Shapiro, G., Frawley, W. (eds.) Knowledge Discovery in Databases, pp. 229–248. AAAI Press/MIT Press, Cambridge, MA (1991)Google Scholar
  35. 35.
    Mata, J., Alvarez, J.L., Riquelme, J.C.: Mining Numeric Association Rules with Genetic Algorithms. In: 5th International Conference on Artificial Neural Networks and Genetic Algorithms, praga ICANNGA, pp. 264–267 (2001)Google Scholar
  36. 36.
    Ghosh, S., Biswas, S., Sarkar, D., Sarkar, P.P.: Mining Frequent Itemsets Using Genetic Algorithm. International Journal of Artificial Intelligence & Applications (IJAIA) 1(4), 133–143 (2010)CrossRefGoogle Scholar
  37. 37.
    Maji, P.K., Biswas, R., Roy, A.R.: Soft set theory. Computers and Mathematics with Applications 45, 555–562 (2003)CrossRefMATHMathSciNetGoogle Scholar
  38. 38.
    Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 207–216 (1993)Google Scholar
  39. 39.
    Handl, J., Kell, D.B.: Multi-objective Optimization in Bioinformatics and Computational Biology. Transactions on Computational Biology, 283 (2007)Google Scholar
  40. 40.
    Ibrahim, C.: Consumption universes based supermarket layout through association rule mining and multi dimensional scaling. Expert Systems with Applications (February 2012)Google Scholar
  41. 41.
    Delgado, G., Aranda, V., Calero, J., Sánchez-Marañón, M., Serrano, J.M., Sánchez, D., Vila, M.A.: Using fuzzy data mining to evaluate survey data from olive grove cultivation. Computers and Electronics in Agriculture 65(1), 99–113 (2009)CrossRefGoogle Scholar
  42. 42.
    Abdullah, Z., Herawan, T., Ahmad, N., Deris, M.M.: Mining significant association rules from educational data using critical relative support approach. Procedia - Social and Behavioral Sciences 28, 97–101 (2011)CrossRefGoogle Scholar
  43. 43.
    Yan, X., Zhang, C., Zhang, S.: Genetic algorithm-based strategy for identifying association rules without specifying actual minimum support. Expert Systems with Applications 36(2), 3066–3076 (2009)CrossRefGoogle Scholar
  44. 44.
    Mata, J., Alvarez, J., Riquelme, J.: An Evolutionary Algorithm to Discover Numeric Association Rules. ACM Symposium on Applied Computing (March 2002)Google Scholar
  45. 45.
    Mata, J., Alvarez, J., Riquelme, J.: Mining Numeric Association Rules with Genetic Algorithms. In: 5th International Conference on Artificial Neural Networks and Genetic Algorithms, Taipei, Taiwan (April 2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.School of Computer ApplicationKIIT University, PatiaBhubaneswarIndia
  2. 2.Department of Information and Communication TechnologyFakir Mohan UniversityBalasoreIndia

Personalised recommendations