Advertisement

Data Mining Analysis on Italian Family Preferences and Expenditures

  • Paola Annoni
  • Pier Alda Ferrari
  • Silvia Salini
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4065)

Abstract

Italian expenditures are a complex system. Every year the Italian National Bureau of Statistics (ISTAT) carries out a survey on the expenditure behavior of Italian families. The survey regards household expenditures on durable and daily goods and on various services. Our goal is here twofold: firstly we describe the most important characteristics of family behavior with respect to expenditures on goods and usage of different services; secondly possible relationships among these behaviors are highlighted and explained by social-demographical features of families. Different data mining techniques are jointly used to these aims so as to identify different capabilities of selected methods within these kinds of issues. In order to properly focalize on service usage, further investigation will be needed about the nature of investigated services (private or public) and, most of all, about their supply and effectiveness along the national territory.

Keywords

Service Usage Association Rule Correspondence Analysis Canonical Correspondence Analysis Family Characteristic 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Agrawal, R., Mannila, H., Srikant, R., Toivonen, H., Verkamo, A.I.: Fast discovery of association rules. In: Advances in Knowledge Discovery and Data Mining. AAAI/MIT Press, Cambridge (1995)Google Scholar
  2. 2.
    Berry, M.J.A., Linoff, G.: Data Mining Techniques: for marketing, sales, and customer support. John Wiley and Sons, Chichester (1996)Google Scholar
  3. 3.
    Breiman, L., Friedman, J.H., Olshen, R., Stone, C.J.: Classification and Regression Trees. Wadsworth (1984)Google Scholar
  4. 4.
    Carbonaro, G.: Nota Sulle Scale di Equivalenza, on La Povertà in Italia, Presidenza del Consiglio dei Ministri, Istituto Poligrafico dello Stato, Rome (1985)Google Scholar
  5. 5.
    Giudici, P.: Applied Data Mining: Statistical Methods for Business and Industry. John Wiley and Sons, Chichester (2003)MATHGoogle Scholar
  6. 6.
    Greenacre, M.J.: Theory and Applications of Correspondence Analysis. Academic Press, London (1984)MATHGoogle Scholar
  7. 7.
    Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning: data mining, inference and prediction. Springer, New York (2001)MATHGoogle Scholar
  8. 8.
    Kenett, R., Thyregod, P.: Aspects of statistical consulting not taught by academia. Neerlandica special issue on Industrial Statistics (to appear, 2006)Google Scholar
  9. 9.
    Perner, P.: Advances in Data Mining. Springer, Heidelberg (2002)MATHGoogle Scholar
  10. 10.
    ter Braak, C.J.F.: Canonical community ordination. Part I: Basic theory and linear methods. Ecoscience 1(2), 127–140 (1994)Google Scholar
  11. 11.
    ter Braak, C.J.F., Verdonschot, P.F.M.: Canonical Correspondence Analysis and related multivariate methods in aquatic ecology. Aquatic Sciences 57/3, 255–289 (1995)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Paola Annoni
    • 1
  • Pier Alda Ferrari
    • 1
  • Silvia Salini
    • 1
  1. 1.Department of Economics, Business and StatisticsUniversity of MilanMilanoItaly

Personalised recommendations