François Poulet


In this paper, we present our work in a new data mining approach called Visual Data Mining (VDM). This new approach tries to involve the user (being the data expert not a data mining or analysis specialist) more intensively in the data mining process and to increase the part of the visualisation in this process. The visualisation part can be increased with cooperative tools: the visualisation is used as a pre- or post-processing step of usual (automatic) data mining algorithms, or the visualisation tools can be used instead of the usual automatic algorithms. All these topics are addressed in this paper with an evaluation of the algorithms presented and a discussion of the interactive algorithms compared with automatic ones. All this work must be improved in order to allow the data specialists to efficiently use these kinds of algorithms to solve their problems.


  1. Ankerst M., Elsen C., Ester M., Kriegel H-P.: "PerceptionBased Classification", in Informatica, An International Journal of Computing and Informatics, 23(4), 493- 499, 1999.
  2. Bennett K. and Campbell C., 2000, “Support Vector Machines: Hype or Hallelujah?”, in SIGKDD Explorations, Vol. 2, No. 2, pp. 1-13.
  3. Blake C., Merz C., UCI Repository of machine learning databases, [], Irvine, CA: University of California, Department of Information and Computer Science, (1998).
  4. Bock H.H., Diday E., "Analysis of Symbolic Data: Exploratory Methods for Extracting Statistical Information from Complex Data", Springer-Verlag, Berlin-Heidelberg, 2000.
  5. Breiman L., Friedman J.H., Olsen R.A., Stone C.J., "Classification And Regression Trees", Wadsworth, 1984.
  6. Fayyad U., Piatetsky-Shapiro G., Smyth P., Uthurusamy R., 1996, "Advances in Knowledge Discovery and Data Mining", AAAI Press.
  7. Inselberg A., Avidan T., "Classification and Visualization for High-Dimensional Data", in proc. of KDD'2000, pp.370-374.
  8. Murthy S., Kasif S., Salzberg S., Beigel R., "OC1: Randomized induction of oblique decision trees", in proc. of the 11th National Conference on Artificial Intelligence, MIT Press, 1993, pp.322-327.
  9. Poulet F. "Full-View: a Visual Data Mining Environment" in International Journal of Image and Graphics, 2(1), 2002.
  10. Poulet F. "Cooperation Between Automatic Algorithms, Interactive Algorithms and Visualization Tools for Visual Data Mining" in proc. of VDM@ECML/PKDD'2002, 2nd Int. Workshop on Visual Data Mining, Helsinki, Aug.2002.
  11. Poulet F., Do, T-N., "Mining Very Large Datasets with Support Vector Machine Algorithms", in proc of ICEIS'2003, 5th Int. Conf. on Enterprise Information Systems, Angers, France, April 2003.
  12. Poulet F. "Interactive Decision Tree Construction for Interval and Taxonomical Data" in proc. of VDM@ICDM'03, the 3rd International Workshop on Visual Data Mining, Melbourne, Florida, Nov.2003.
  13. Quinlan J.R., "C4.5: Programs for Machine Learning", Morgan-Kaufman Publishers, 1993.
  14. Vapnik V., 1995, "The Nature of Statistical Learning Theory", Springer-Verlag, New York.

Paper Citation

in Harvard Style

Poulet F. (2004). TOWARDS VISUAL DATA MINING . In Proceedings of the Sixth International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 972-8865-00-7, pages 349-356. DOI: 10.5220/0002639703490356

in Bibtex Style

author={François Poulet},
booktitle={Proceedings of the Sixth International Conference on Enterprise Information Systems - Volume 2: ICEIS,},

in EndNote Style

JO - Proceedings of the Sixth International Conference on Enterprise Information Systems - Volume 2: ICEIS,
SN - 972-8865-00-7
AU - Poulet F.
PY - 2004
SP - 349
EP - 356
DO - 10.5220/0002639703490356