A Methodology for Exploring Association Models

  • Alipio Jorge
  • João Poças
  • Paulo J. Azevedo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4404)


Visualization in data mining is typically related to data exploration. In this chapter we present a methodology for the post processing and visualization of association rule models. One aim is to provide the user with a tool that enables the exploration of a large set of association rules. The method is inspired by the hypertext metaphor. The initial set of rules is dynamically divided into small comprehensible sets or pages, according to the interest of the user. From each set, the user can move to other sets by choosing one appropriate operator. The set of available operators transform sets of rules into sets of rules, allowing focusing on interesting regions of the rule space. Each set of rules can also be then seen with different graphical representations. The tool is web-based and dynamically generates SVG pages to represent graphics. Association rules are given in PMML format.


Data Mining Association Rule Scalable Vector Graphic Data Mining Model Document Object Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Agrawal, R., Mannila, H., Srikant, R., Toivonen, H., Verkamo, A.I.: Fast Discovery of Association Rules. Advances in Knowledge Discovery and Data Mining, 307–328 (1996)Google Scholar
  2. 2.
    Azevedo, P.J.: CAREN – A Java based Apriori Implementation for Classification Purposes, Technical Report, Departamento de Informática, Universidade do Minho,
  3. 3.
    Berry, M.J.A., Linoff, G.S.: Data Mining Techniques: For Marketing, Sales, and Customer Support. John Wiley & Sons, Chichester (1997)Google Scholar
  4. 4.
    Brijs, T., Swinnen, G., Vanhoof, K., Wets, G.: Building an Association Rules Framework to Improve Product Assortment Decisions. Data Min. Knowl. Discov. 8(1), 7–23 (2004)CrossRefMathSciNetGoogle Scholar
  5. 5.
    Brin, S., Motwani, R., Ullman, J.D., Tsur, S.: Dynamic itemset counting and implication rules for market basket data. SIGMOD Record (ACM Special Interest Group on Man-agement of Data) 26(2), 255 (1997), Google Scholar
  6. 6.
    Chen, M.-C., Lin, C.-P.: A data mining approach to product assortment and shelf space allocation. In: Expert Systems with Applications. Elsevier, Amsterdam (2006)Google Scholar
  7. 7.
    Chang, H.-J., Hung, L.-P., Ho, C.-L.: An anticipation model of potential customers’ pur-chasing behavior based on clustering analysis and association rules analysis. In: Expert Systems with Applications. Elsevier, Amsterdam (2007)Google Scholar
  8. 8.
    Clementine Software, SPSS,
  9. 9.
    Data Mining Group (PMML development),
  10. 10.
    Demiriz, A.: Enhancing Product Recommender Systems on Sparse Binary Data. Data Min. Knowl. Discov. 9(2), 147–170 (2004)CrossRefMathSciNetGoogle Scholar
  11. 11.
    Jorge, A.: Hierarchical Clustering for thematic browsing and summarization of large sets of Association Rules. In: Proceedings of the Fourth SIAM International Conference on Data Mining, pp. 178–187. SIAM press, Philadelphia (2004)Google Scholar
  12. 12.
    Jorge, A., Poças, J., Azevedo, P.: Post-processing operators for browsing large sets of association rules. In: Lange, S., Satoh, K., Smith, C.H. (eds.) DS 2002. LNCS, vol. 2534, pp. 414–421. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  13. 13.
    Lent, B., Swami, A., Widom, J.: Clustering Association Rules. In: Gray, A., Larson, P. (eds.) Proc. of the Thirteenth International Conference on Data Engineering, ICDE 1997, IEEE Computer Society, Birmingham (1997)Google Scholar
  14. 14.
    Kandogan, E.: Visualizing Multi-dimensional Clusters, Trends and Outliers using Star Coordinates. In: Proceedings of KDD 2001, ACM Press, New York (2001)Google Scholar
  15. 15.
    Klemettinen, M., Mannila, H., Ronkainen, P., Toivonen, H., Verkamo, A.: Finding interesting rules from large sets of discovered association rules. In: Nabil, R., et al. (eds.) Proceedings of 3rd International Conference on Information and Knowledge Management, pp. 401–407. ACM Press (1994)Google Scholar
  16. 16.
    Li, W., Han, J., Pei, J.: CMAR: Accurate and Efficient Classification Based on Multiple-Class-Association Rules. In: IEEE International Conference on Data Mining (2001),
  17. 17.
    Ma, Y., Liu, B., Wong, K.: Web for Data Mininig: Organizing and Inter-preting the Discovered Rules Using the Web, School. SIGKDD Explorations, ACM SIGKDD 2(1) (July 2000)Google Scholar
  18. 18.
    Microsoft Web Site (JScript and JavaScript) and ASP,,
  19. 19.
    Poças, J.: Um ambiente de pós-processamento para regras de associação. MSc. Thesis in Portuguese, Mestrado em Análise de Dados e Sistemas de Apoio à Decisão (2003)Google Scholar
  20. 20.
    Savasere, A., Omiecinski, E., Navathe, S.: An efficient algorithm for mining association rules in large databases. In: Proc. of 21st Intl. Conf. on Very Large Databases (VLDB) (1995)Google Scholar
  21. 21.
    Silberschatz, A., Tuzhilin, A.: On subjective measures of interestingness in knowledge discovery. In: Proceedings of the First International Conference on Knowledge Discovery and Data Mining, pp. 275–281 (1995),
  22. 22.
    Tan, P.-N., Kumar, V.: Interestingness measures for association patterns: a perspective. In: Proceedings of the Workshop on Post-processing in Machine Learning and Data Mining, associated to KDD 2000 (2000)Google Scholar
  23. 23.
    Toivonen, H.: Sampling large databases for association rules. In: Proc. of 22nd Intl. Conf. on Very Large Databases (VLDB) (1996),
  24. 24.
    W3C DOM Level 1 specification,
  25. 25.
    W3C, Scalable Vector Graphics (SVG) 1.0 Specification, W3C Recommendation (September 2001),
  26. 26.
    Wettshereck, D.: A KDDSE-independent PMML Visualizer. In: Bohanec, M., Mladenic, D., Lavrac, N. (eds.) Proc. of IDDM 2002, workshop on Integration aspects of Decision Support and Data Mining. associated to the conferences ECML/PKDD (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Alipio Jorge
    • 1
  • João Poças
    • 2
  • Paulo J. Azevedo
    • 3
  1. 1.LIACC/FEPUniversidade do PortoPortugal
  2. 2.Instituto Nacional de EstatísticaPortugal
  3. 3.Departamento de InformáticaUniversidade do MinhoPortugal

Personalised recommendations