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Realizing New Hybrid Rough Fuzzy Association Rule Mining Algorithm (RFA) Over Apriori Algorithm

  • Aritra Roy
  • Rajdeep Chatterjee
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 308)

Abstract

Association rules shows us interesting associations among data items. And the procedure by which these rules are extracted and managed is known as association rule mining. Classical association rule mining had many limitations. Fuzzy association rule mining (Fuzzy ARM) is a better alternative of classical association rule mining. But fuzzy ARM also has its limitations like redundant rule generation and inefficiency in large mining tasks. Rough association rule mining (Rough ARM) seemed to be a better approach than fuzzy ARM. Mining task is becoming huge now days. Performing mining task efficiently and accurately over a large dataset is still a big challenge to us. This paper presents the realization of new hybrid mining method which has incorporated the concepts of both rough set theory and fuzzy set theory for association rule generation and shows comparative analysis with Apriori algorithm based on test results of the algorithm over popular datasets.

Keywords

Association rule mining Fuzzy association rule mining Fuzzy c-means clustering Rough set theory Rough association rule mining Attribute reduction Apriori algorithm 

References

  1. 1.
    Frawley, W.J., Piatetsky-Shapiro, G., Matheus, C.J.: Knowledge Discovery in Databases: An Overview. AAAI/MIT Press, Cambridge (1992)MATHGoogle Scholar
  2. 2.
    Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann, Los Altos (2001)MATHGoogle Scholar
  3. 3.
  4. 4.
    Zadeh, L.A.: Fuzzy sets. Inf. Control 8, 338–358 (1965)MathSciNetCrossRefMATHGoogle Scholar
  5. 5.
    Roy, A., Chatterjee, R.: A survey on fuzzy association rule mining methodologies. IOSR J. Comput. Eng. (IOSR-JCE), e-ISSN: 2278-0661, p-ISSN: 2278-8727, 15(6), 1–8 (2013)Google Scholar
  6. 6.
    Mangalampalli, A., Pudi, V.: Fuzzy association rule mining algorithm for fast and efficient performance on very large datasets. In: FUZZ-IEEE 2009, Korea, ISSN: 1098-7584, E-ISBN: 978-1-4244-3597-5, pp. 1163–1168, 20–24 Aug 2009Google Scholar
  7. 7.
    Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Kluwer Academic Publishers, Norwell (1981)CrossRefMATHGoogle Scholar
  8. 8.
    Hoppner, F., Klawonn, F., Kruse, R., Runkler, T.: Fuzzy Cluster Analysis, Methods for Classification, Data Analysis and Image Recognition. Wiley, New York (1999)MATHGoogle Scholar
  9. 9.
    Mahmoudi, EV, Aghighi, V, Torshiz, MN, Jalali, M., Yaghoobi, M.: Mining generalized fuzzy association rules via determining minimum supports. In: IEEE Iranian Conference on Electrical Engineering (ICEE), E-ISBN: 978-964-463-428-4, Print ISBN: 978-1-4577-0730-8, pp. 1–6 (2011)Google Scholar
  10. 10.
    Watanabe, T.: Fuzzy association rules mining algorithm based on output specification and redundancy of rules. In: IEEE International Conference on Systems, Man, and Cybernetics (SMC), ISSN: 1062-922X, Print ISBN: 978-1-4577-0652-3, pp. 283–289 (2011)Google Scholar
  11. 11.
    Delgado, M., Marin, N., Martin-Bautista, M.J., Sanchez, D., Vila, M.-A.: Mining fuzzy association rules: An overview. In: Studies in Fuzziness and Soft Computing, vol. 164/2005, pp. 351–373. Springer, Berlin (2006)Google Scholar
  12. 12.
    Delgado, M., Marin, N., Sanchez, D., Vila, M.-A.: Fuzzy association rules: general model and applications. IEEE Trans. Fuzzy Syst. 11(2), 214–225 (2003)CrossRefGoogle Scholar
  13. 13.
    Watanabe, T., Fujioka, R.: Fuzzy association rules mining algorithm based on equivalence redundancy of items. IEEE Trans. Syst. Man Cybern. E-ISBN: 978-1-4673-1712-2, Print ISBN: 978-1-4673-1713-9, 1960–1965 (2012)Google Scholar
  14. 14.
    Pawlak, Z.: Rough sets. Int. J. Comput. Inf. Sci. 11(5), 341–356 (1982)MathSciNetCrossRefMATHGoogle Scholar
  15. 15.
    Guan, J.W., Bell, D.A., Liu, D.Y.: The rough set approach to association rule mining. In: Proceedings of the Third IEEE International Conference on Data Mining (ICDM’03), Print ISBN: 0-7695-1978-4, pp. 529–532 (2003)Google Scholar
  16. 16.
    Feldman, R., Aumann, Y., Amir, A., Zilberstain, A., Kloesgen, W., Ben-Yehuda, Y.: Maximal association rules: a new tool for mining for keyword co-occurrences in document collection. In: Proceedings of the 3rd International Conference on Knowledge Discovery, pp. 167–170 (1997)Google Scholar
  17. 17.
    Chu-xiang, C., Jian-jing, S., Bing, C., Chang-xing, S., Yun-cheng, W.: An improvement apriori arithmetic based on rough set theory. In: Third Pacific-Asia Conference on Circuits, Communications and System (PACCS), Print ISBN: 978-1-4577-0855-8, pp. 1–3 (2011)Google Scholar
  18. 18.
    Jiao, X., Lian-cheng, X., Lin, Q.: Association rules mining algorithm based on rough set. In: International Symposium on Information Technology in Medicine and Education, Print ISBN: 978-1-4673-2109-9, Vol 1, pp. 361–364 (2012)Google Scholar
  19. 19.
    Pawlak, Z.: Rough Sets Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Dordrecht (1991)MATHGoogle Scholar
  20. 20.
    Suraj, Z.: An introduction to rough set theory and its applications: a tutorial. ICENCO, (2004)Google Scholar
  21. 21.
    Brown, E.M.: Boolean Reasoning. Kluwer Academic Publishers, Dordrecht (1990)CrossRefMATHGoogle Scholar
  22. 22.
    Dunn, J.C.: A fuzzy relative of the ISODATA process and its use in detecting compact well separated clusters. J. Cybern. Syst. 3, 32–57 (1974)MathSciNetCrossRefMATHGoogle Scholar
  23. 23.
    Komorowski, J., Pawlak, Z., Polkowski, L., Skowron, A.: Rough sets: a tutorial. In: Pal, S.K., Skowron, A. (eds.) Rough Fuzzy Hybridization. A New Trend in Decision-Making, pp. 3–98. Springer, Berlin (1999)Google Scholar
  24. 24.
    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 (1993)Google Scholar
  25. 25.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Proceedings of the 20th International Conference on Very Large Data Bases, VLDB, pp. 487–499 (1994)Google Scholar
  26. 26.
    UCI machine learning repository: https://archive.ics.uci.edu/ml/datasets.html
  27. 27.
  28. 28.

Copyright information

© Springer India 2015

Authors and Affiliations

  1. 1.School of Computer EngineeringKIIT UniversityBhubaneswarIndia

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