A Hybrid Technique for Data Mining on Balance-Sheet Data

  • G. Dattilo
  • S. Greco
  • E. Masciari
  • L. Pontieri
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1874)

Abstract

Recent rapid growth in the ability to generate and store data by more powerful Database Management Systems and hardware architecture, leads to a question: how can we take advantage of this large amount of information? Traditional methods for querying and reporting are inadequate because they can only manipulate data and the information content derived is very low. Obtaining new relationships among data and new hypotheses about them is the aim of Knowledge Discovery in Databases (KDD) which makes use of Data Mining techniques. These techniques have interesting applications for business data such as market basket analysis, financial resource planning, fraud detection and the scheduling of production processes. In this work we consider the application of Data Mining techniques for the analysis of the balance-sheets of Italian companies.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Mardia, K.V., Kent, J.T., Bibby, J.M.: Multivariant Analysis. Academic Press, New York (1979)Google Scholar
  2. 2.
    Chen, M.S., Han, J., Yu, P.S.: Data Mining: An Overview from a Database Perspective. In: IEEE Trans, on Know. Disc, and Data Eng. 8 (1996) 866–883CrossRefGoogle Scholar
  3. 3.
    Cheeseman, P., Stutz, J. Bayesian Classification (Autoclass): Theory and Results. In: Piatesky-Shapiro, G., Smyth, P. (eds): Advances in Knoweldge Discovery and Data Mining. The MIT Press, Menlo Park [6] (1996) 153–180Google Scholar
  4. 4.
    Dougherty, J., Kohavi, R., Sahami. M.: Supervised and unsupervised discretization of continuous features. In: Machine Learning, Proc. of the 12th Int. Conf. on Machine Learning. Morgan Kaufmann (1997) 194–202Google Scholar
  5. 5.
    Fayyad, U.M., Piatesky-Shapiro, G., Smyth, P.: From Data Mining to Knowledge Discovery: An overview. In Piatesky-Shapiro, G., Smyth, P. (eds): Advances in Knoweldge Discovery and Data Mining. The MIT Press, Menlo Park [6] (1996) 1–36Google Scholar
  6. 6.
    Fayyad, U.M., Piatesky-Shapiro, G., Smyth, P. (eds): Advances in Knoweldge Discovery and Data Mining. The MIT Press, Menlo Park (1996)Google Scholar
  7. 7.
    Quinlan, J.R.: Induction of Decision Trees. Machine Learning 1 (1986) 81–106Google Scholar
  8. 8.
    Salzberg, S.L.: On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach. Data Mining and Knowledge Discovery 1 (1997) 317–328CrossRefGoogle Scholar
  9. 9.
    Scheffer, T., Herbrich, H.: Unbiased assessment of learning algorithm. In: Proc. 15th Int. Joint Conf. on Artif. Intell. Morgan Kaufmann (1997) 798–803Google Scholar
  10. 10.
    Hanson, R., Stutz, J., Cheeseman, P.: Bayesian classification with correlation and inheritance. In: Proc. 12th Int. Joint Conf. on Artif. Intell. Morgan Kaufmann (1991) 692–698Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • G. Dattilo
    • 2
  • S. Greco
    • 1
    • 2
  • E. Masciari
    • 1
    • 2
  • L. Pontieri
    • 1
    • 2
  1. 1.DEIS, Univ. della CalabriaRendeItaly
  2. 2.ISI-CNRRendeItaly

Personalised recommendations