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SMARViz: Soft Maximal Association Rules Visualization

  • Tutut Herawan
  • Iwan Tri Riyadi Yanto
  • Mustafa Mat Deris
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5857)

Abstract

Maximal association rule is one of the popular data mining techniques. However, no current research has found that allow for the visualization of the captured maximal rules. In this paper, SMARViz (Soft Maximal Association Rules Visualization), an approach for visualizing soft maximal association rules is proposed. The proposed approach contains four main steps, including discovering, visualizing maximal supported sets, capturing and finally visualizing the maximal rules under soft set theory.

Keywords

Data mining Maximal association rules Soft set theory Visualization 

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References

  1. 1.
    Feldman, R., Aumann, Y., Amir, A., Zilberstein, A., Klosgen, W.: Maximal association rules: a new tool for mining for keywords cooccurrences in document collections. In: Proceedings of the KDD 1997, pp. 167–170 (1997)Google Scholar
  2. 2.
    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 the Management of Data, pp. 207–216 (1993)Google Scholar
  3. 3.
    Guan, J.W., Bell, D.A., Liu, D.Y.: The Rough Set Approach to Association Rule Mining. In: Proceedings of the Third IEEE ICDM 2003, pp. 529–532 (2003)Google Scholar
  4. 4.
    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
  5. 5.
    Guan, J.W., Bell, D.A., Liu, D.Y.: Mining Association Rules with Rough Sets. SCI, pp. 163–184. Springer, Heidelberg (2005)Google Scholar
  6. 6.
    Pawlak, Z.: Rough sets. International Journal of Computer and Information Science 11, 341–356 (1982)zbMATHCrossRefMathSciNetGoogle Scholar
  7. 7.
    Pawlak, Z.: Rough sets: A theoretical aspect of reasoning about data. Kluwer Academic Publisher, Dordrecht (1991)Google Scholar
  8. 8.
    Pawlak, Z., Skowron, A.: Rudiments of rough sets. Information Sciences 177(1), 3–27 (2007)zbMATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    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
  10. 10.
    Herawan, T., Mustafa, M.D.: A soft set approach for maximal association rules mining (submitted 2009)Google Scholar
  11. 11.
    Herawan, T., Mustafa, M.D.: A direct proof of every rough set is a soft set. In: Proceeding of International Conference AMS 2009 (2009)Google Scholar
  12. 12.
    Wong, P.C., Whitney, P., Thomas, J.: Visualizing Association Rules for Text Mining. In: Proceeding of IEEE INFOVIS 1999, pp. 120–123 (1999)Google Scholar
  13. 13.
    Bruzzese, D., Buono, P.: Combining Visual Techniques for Association Rules Exploration. In: Proceedings of the working conference on Advanced Visual Interfaces, AVI 2004, pp. 381–384. ACM Press, New York (2004)CrossRefGoogle Scholar
  14. 14.
    Ceglar, A., Roddick, J., Calder, P., Rainsford, C.: Visualising hierarchical associations. Knowledge and Information Systems 8, 257–275 (2005)CrossRefGoogle Scholar
  15. 15.
    Kopanakis, I., Pelekis, N., Karanikas, H., Mavroudkis, T.: Visual Techniques for the Interpretation of Data Mining Outcomes. In: Bozanis, P., Houstis, E.N. (eds.) PCI 2005. LNCS, vol. 3746, pp. 25–35. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  16. 16.
    Lopes, A.A., Pinho, R., Paulovich, F.V., Minghim, R.: Visual text mining using association rules. Computers & Graphics 31, 316–326 (2007)CrossRefGoogle Scholar
  17. 17.
    Leung, C.K.S., Irani, P., Carmichael, C.L.: WiFIsViz: Effective Visualization of Frequent Itemsets. In: Proceeding of ICDM 2008, pp. 875–880. IEEE Press, Los Alamitos (2008)Google Scholar
  18. 18.
    Leung, C.K.S., Irani, P., Carmichael, C.L.: FIsViz: A Frequent Itemset Visualizer. In: Washio, T., Suzuki, E., Ting, K.M., Inokuchi, A. (eds.) PAKDD 2008. LNCS (LNAI), vol. 5012, pp. 644–652. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  19. 19.
    Leung, C.K.S., Carmichael, C.L.: FpViz: A Visualizer for Frequent Pattern Mining. In: Proceeding of VAKD 2009, pp. 30–49. ACM Press, New York (2009)Google Scholar
  20. 20.
    Molodtsov, D.: Soft set theory-first results. Computers and Mathematics with Applications 37, 19–31 (1999)zbMATHCrossRefMathSciNetGoogle Scholar
  21. 21.
    Keim, D.A.: Information Visualization and Visual Data Mining. IEEE transaction on visualization and computer graphics 7, 100–107 (2002)Google Scholar
  22. 22.
  23. 23.
    Mustafa, M.D., Nabila, N.F., Evans, D.J., Saman, M.Y., Mamat, A.: Association rules on significant rare data using second support. International Journal of Computer Mathematics 83(1), 69–80 (2006)zbMATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Tutut Herawan
    • 1
    • 2
  • Iwan Tri Riyadi Yanto
    • 1
    • 2
  • Mustafa Mat Deris
    • 2
  1. 1.CIRNOVUniversitas Ahmad DahlanYogyakartaIndonesia
  2. 2.FTMMUniversiti Tun Hussein Onn MalaysiaMalaysia

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