Modeling Interestingness of Streaming Classification Rules as a Classification Problem

  • Tolga Aydın
  • Halil Altay Güvenir
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3949)


Inducing classification rules on domains from which information is gathered at regular periods lead the number of such classification rules to be generally so huge that selection of interesting ones among all discovered rules becomes an important task. At each period, using the newly gathered information from the domain, the new classification rules are induced. Therefore, these rules stream through time and are so called streaming classification rules. In this paper, an interactive classification rules’ interestingness learning algorithm (ICRIL) is developed to automatically label the classification rules either as “interesting” or “uninteresting” with limited user interaction. In our study, VFFP (Voting Fuzzified Feature Projections), a feature projection based incremental classification algorithm, is also developed in the framework of ICRIL. The concept description learned by the VFFP is the interestingness concept of streaming classification rules.


Linguistic Term Classification Rule Training Instance Nominal Feature User Participation 
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.
    Güvenir, H.A.: Benefit Maximization in Classification on Feature Projections. In: Proceedings of the 3rd IASTED International Conference on Artificial Intelligence and Applications (AIA 2003), pp. 424–429 (2003)Google Scholar
  2. 2.
    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 3rd Int. Conf. on Information and Knowledge Management, pp. 401–407 (1994)Google Scholar
  3. 3.
    Liu, B., Hsu, W., Chen, S.: Using general impressions to analyze discovered classification rules. In: Proceedings of the 3rd Int. Conf. on KDD, pp. 31–36 (1997)Google Scholar
  4. 4.
    Liu, B., Hsu, W.: Post-analysis of learned rules, pp. 828–834. AAAI Press, Menlo Park (1996)Google Scholar
  5. 5.
    Hussain, F., Liu, H., Suzuki, E., Lu, H.: Exception rule mining with a relative interestingness measure. In: Terano, T., Chen, A.L.P. (eds.) PAKDD 2000. LNCS, vol. 1805, pp. 86–97. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  6. 6.
    Dong, G., Li, J.: Interestingness of discovered association rules in terms of neighborhood-based unexpectedness. In: Wu, X., Kotagiri, R., Korb, K.B. (eds.) PAKDD 1998. LNCS, vol. 1394, pp. 72–86. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  7. 7.
    Aydın, T., Güvenir, H.A.: Learning Interestingness of Streaming Classification Rules. In: Aykanat, C., Dayar, T., Körpeoğlu, İ. (eds.) ISCIS 2004. LNCS, vol. 3280, pp. 62–71. Springer, Heidelberg (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Tolga Aydın
    • 1
  • Halil Altay Güvenir
    • 1
  1. 1.Department of Computer EngineeringBilkent UniversityAnkaraTurkey

Personalised recommendations