Visual Mining of Association Rules

  • Dario Bruzzese
  • Cristina Davino
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4404)


Association Rules are one of the most widespread data mining tools because they can be easily mined, even from very huge database, and they provide valuable information for many application fields such as marketing, credit scoring, business, etc. The counterpart is that a massive effort is required (due to the large number of rules usually mined) in order to make actionable the retained knowledge. In this framework vizualization tools become essential to have a deep insight into the association structures and interactive features have to be exploited for highlighting the most relevant and meaningful rules.


Data Mining Association Rule Mining Association Rule Multiple Correspondence Analysis Factorial Plane 
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.


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  1. 1.
    Advanced Visual Systems (AVS), OpenViz.
  2. 2.
    Agrawal, R., Imielinski, T., Swami, A.: Mining Association Rules between Sets of Items in Large Databases. In: Proceedings of the 1993 ACM SIGMOD Conference, Washington DC, USA, pp. 207–216 (May 1993)Google Scholar
  3. 3.
    Benzècri, J.-P.: L’Analyse des Donnèes, Dunod, Paris (1973)Google Scholar
  4. 4.
    Bruzzese, D., Buono, P.: Combining Visual Techniques for Association Rules Exploration. In: Proceedings of the International Conference Advances Visual Interfaces, Gallipoli, Italy, May 25-28 (2004)Google Scholar
  5. 5.
    Bruzzese, D., Davino, C., Vistocco, D.: Parallel Coordinates for Interactive Exploration of Association Rules. In: Proceedings of the 10th International Conference on Human - Computer Interaction, Creta, Greece, June 22-27. Lawrence Erlbaum, Mahwah (2003)Google Scholar
  6. 6.
    Bruzzese, D., Davino, C.: Significant Knowledge Extraction from Association Rules. In: Electronic Proceedings of the International Conference Knowledge Extraction and Modeling Workshop, Anacapri, Italy, September 4-6 (2006)Google Scholar
  7. 7.
    Clementine, Suite from SPSS,
  8. 8.
    Glymour, C., Madigan, D., Pregibon, D., Smyth, P.: Statistical Inference and Data Mining. Communications of the ACM (1996)Google Scholar
  9. 9.
    Greenacre, M.: Correspondence Analysis in Practice. Academic Press, London (1993)Google Scholar
  10. 10.
    Hartigan, J., Kleiner, B.: Mosaics for contingency tables. In: Proceedings of the 13th Symposium on the interface, pp. 268–273 (1981)Google Scholar
  11. 11.
    Hofmann, H.: Exploring categorical data: interactive mosaic plots. Metrika 51(1), 11–26 (2000)zbMATHCrossRefGoogle Scholar
  12. 12.
    Hofmann, H., Siebes, A., Wilhelm, A.: Visualizing Association Rules with Interactive Mosaic Plots. In: Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 227–235 (2000)Google Scholar
  13. 13.
    Hofmann, H., Wilhelm, A.: Validation of Association Rules by Interactive Mosaic Plots. In: Bethlehem, J.G., van der Heijden, P.G.M. (eds.) Compstat 2000 - Proceedings in Computational Statistics, pp. 499–504. Physica-Verlag, Heidelberg (2000)Google Scholar
  14. 14.
    Hofmann, H., Wilhelm, A.: Visual Comparison of Association Rules. Computational Statistics 16, 399–416 (2001)zbMATHCrossRefMathSciNetGoogle Scholar
  15. 15.
    IBM Intelligent Miner for Data,
  16. 16.
    Inselberg, A.: N-dimensional Graphics, part I - Lines and Hyperplanes, in IBM LASC Tech. Rep. G320-2711, 140 pages. IBM LA Scientific Center (1981)Google Scholar
  17. 17.
    Inselberg, A.: Visual Data Mining with Parallel Coordinates. Computational Statistics 13(1), 47–64 (1998)zbMATHGoogle Scholar
  18. 18.
    Klemettinen, M., Mannila, H., Ronkainen, P., Toivonen, H., Verkamo, A.I.: Finding interesting rules from large sets of discovered association rules. In: Proceedings of the Third International Conference on Information and Knowledge Management CIKM 1994, pp. 401–407 (1994)Google Scholar
  19. 19.
    Kopanakis, I., Theodoulidis, B.: Visual Data Mining & Modeling Techniques. In: 4th International Conference on Knowledge Discovery and Data Mining (2001)Google Scholar
  20. 20.
    Liu, B., Hsu, W., Ma, Y.: Pruning and Summarizing the Discovered Associations. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 1999), San Diego, CA, USA, August 15-18 (1999)Google Scholar
  21. 21.
    Liu, B., Ma, Y., Lee, R.: Analyzing the Interestingness of Association Rules from Temporal Dimensions. In: International Conference on Data Mining, CA (2001)Google Scholar
  22. 22.
  23. 23.
    Megiddo, N., Srikant, R.: Discovering Predictive Association Rules. In: Knowledge Discovery and Data Mining (KDD 1998), pp. 274–278 (1998)Google Scholar
  24. 24.
    Miner3D, Miner3D Excel,
  25. 25.
    Ong, K.-H., Ong, K.-L., Ng, W.-K., Lim, E.-P.: CrystalClear: active Visualization of Association Rules. In: Proc. of the Int. workshop on Active Mining, Japan (2002)Google Scholar
  26. 26.
    Purple Insight Mineset,
  27. 27.
  28. 28.
    Shah, D., Lakshmanan, L.V.S., Ramamritham, K., Sudarshan, S.: Interestingness and Pruning of Mined Patterns. In: Workshop Notes of the 1999 ACM SIGMOD Research Issues in Data Mining and Knowledge Discovery (1999)Google Scholar
  29. 29.
  30. 30.
    Toivonen, H., Klemettinen, M., Ronkainen, P., Hatonen, K., Mannila, H.: Pruning and grouping of discovered association rules. In: Workshop Notes of the ECML-95 Workshop on Statistics, Machine Learning, and Knowledge Discovery in Databases, Heraklion, Greece, April 1995, pp. 47–52 (1995)Google Scholar
  31. 31.
    Unwin, A., Hofmann, H., Bernt, K.: The TwoKey Plot for Multiple Association Rules Control. In: Siebes, A., De Raedt, L. (eds.) PKDD 2001. LNCS (LNAI), vol. 2168. Springer, Heidelberg (2001)Google Scholar
  32. 32.
  33. 33.
    Webb, G.I.: Preliminary Investigations into Statistically Valid Exploratory Rule Discovery. In: Proceedings of the Australasian Data Mining Workshop, Sydney (2003)Google Scholar
  34. 34.
    Weber, I.: On Pruning Strategies for Discovery of Generalized and Quantitative Association Rules. In: Proceedings of Knowledge Discovery and Data Mining Workshop, Singapore (1998)Google Scholar
  35. 35.
    Wong, P.C., Whitney, P., Thomas, J.: Visualizing Association Rules for Text Mining. In: Wills, G., Keim, D. (eds.) Proceedings of IEEE Information Visualization 1999. IEEE CS Press, Los Alamitos (1999)Google Scholar
  36. 36.
    XGvis: A System for Multidimensional Scaling and Graph Layout in any Dimension,
  37. 37.
    Yang, L.: Visualizing Frequent Itemsets, Association Rules and Sequential Patterns in Parallel Coordinates. In: Kumar, V., Gavrilova, M.L., Tan, C.J.K., L’Ecuyer, P. (eds.) ICCSA 2003. LNCS, vol. 2667, pp. 21–30. Springer, Heidelberg (2003)CrossRefGoogle Scholar

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© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Dario Bruzzese
    • 1
  • Cristina Davino
    • 2
  1. 1.Dipartimento di Scienze Mediche PreventiveUniversità di Napoli Federico IINapoliItaly
  2. 2.Dipartimento di Studi sullo Sviluppo EconomicoUniversità di MacerataMacerataItaly

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