Skip to main content
SpringerLink
Log in
Book cover

International Visual Informatics Conference

IVIC 2009: Visual Informatics: Bridging Research and Practice pp 664–674Cite as

SMARViz: Soft Maximal Association Rules Visualization

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNIP,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.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  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. 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. 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. 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)

    Article  Google Scholar 

  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. Pawlak, Z.: Rough sets. International Journal of Computer and Information Science 11, 341–356 (1982)

    Article  MATH  MathSciNet  Google Scholar 

  7. Pawlak, Z.: Rough sets: A theoretical aspect of reasoning about data. Kluwer Academic Publisher, Dordrecht (1991)

    Google Scholar 

  8. Pawlak, Z., Skowron, A.: Rudiments of rough sets. Information Sciences 177(1), 3–27 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  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)

    Article  Google Scholar 

  10. Herawan, T., Mustafa, M.D.: A soft set approach for maximal association rules mining (submitted 2009)

    Google Scholar 

  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. 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. 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)

    Chapter  Google Scholar 

  14. Ceglar, A., Roddick, J., Calder, P., Rainsford, C.: Visualising hierarchical associations. Knowledge and Information Systems 8, 257–275 (2005)

    Article  Google Scholar 

  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)

    Chapter  Google Scholar 

  16. Lopes, A.A., Pinho, R., Paulovich, F.V., Minghim, R.: Visual text mining using association rules. Computers & Graphics 31, 316–326 (2007)

    Article  Google Scholar 

  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. 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)

    Chapter  Google Scholar 

  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. Molodtsov, D.: Soft set theory-first results. Computers and Mathematics with Applications 37, 19–31 (1999)

    Article  MATH  MathSciNet  Google Scholar 

  21. Keim, D.A.: Information Visualization and Visual Data Mining. IEEE transaction on visualization and computer graphics 7, 100–107 (2002)

    Google Scholar 

  22. Reuters-21578 (2002), http://www.research.att.com/lewis/reuters21578.html

  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)

    Article  MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. CIRNOV, Universitas Ahmad Dahlan, Yogyakarta, Indonesia

    Tutut Herawan & Iwan Tri Riyadi Yanto

  2. FTMM, Universiti Tun Hussein Onn Malaysia, Johor, Malaysia

    Tutut Herawan, Iwan Tri Riyadi Yanto & Mustafa Mat Deris

Authors
  1. Tutut Herawan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Iwan Tri Riyadi Yanto
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mustafa Mat Deris
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Universiti Kebangsaan Malaysia, Bangi, Malaysia

    Halimah Badioze Zaman

  2. Computer Laboratory, University of Cambridge, 15 JJ Thomson Avenue, CB3 0FD, Cambridge, United Kingdom

    Peter Robinson

  3. Communications and Signal Processing Group, Electrical and Electronic Engineering Department, Imperial College, SW7 2AZ, London, UK

    Maria Petrou

  4. School of Computing Science, Culture Lab, University of Newcastle upon Tyne, NE1 7RU, United Kingdom

    Patrick Olivier

  5. School of Computer Science and Information Technology, RMIT University, GPO Box 2476, 3001, Melbourne, Victoria, Australia

    Heiko Schröder

  6. Department of Computer Science and Information Engineering, Tamkang University, Tamkang University,, Taiwan

    Timothy K. Shih

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Herawan, T., Yanto, I.T.R., Deris, M.M. (2009). SMARViz: Soft Maximal Association Rules Visualization. In: Badioze Zaman, H., Robinson, P., Petrou, M., Olivier, P., Schröder, H., Shih, T.K. (eds) Visual Informatics: Bridging Research and Practice. IVIC 2009. Lecture Notes in Computer Science, vol 5857. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05036-7_63

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-05036-7_63

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-05035-0

  • Online ISBN: 978-3-642-05036-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation