Greedy Algorithm for Decision Tree Construction in Context of Knowledge Discovery Problems

  • Mikhail Ju. Moshkov
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3066)


In the paper a greedy algorithm for minimization of decision tree depth is considered and bounds on the algorithm precision are discussed. Under some natural assumptions on the class NP and on the class of considered tables, this algorithm is, apparently, close to best approximate polynomial algorithms for minimization of decision tree depth. Unfortunately, the performance ratio of this algorithm grows almost as natural logarithm on the number of rows in the table. Except usual greedy algorithm we study greedy algorithm with threshold which constructs approximate decision trees. Such approach is fully admissible if we see on decision trees as on a way for knowledge representation. We obtain upper bounds on the depth of decision trees, constructed by this algorithms, which are independent of the number of rows in the table.


data table knowledge discovery greedy algorithm approximate decision tree 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and Regression Trees. Wadsworth & Brooks (1984)Google Scholar
  2. 2.
    Feige, U.: A threshold of ln n for approximating set cover (Preliminary version). In: Proceedings of 28th Annual ACM Symposium on the Theory of Computing, pp. 314–318 (1996)Google Scholar
  3. 3.
    Moshkov, M.J.: Conditional tests. In: Yablonskii, S.V. (ed.) Problems of Cybernetics 40, pp. 131–170. Nauka Publishers, Moscow (1983) (in Russian)Google Scholar
  4. 4.
    Moshkov, M.J.: Greedy algorithm of decision tree construction for real data tables. LNCS Transactions on Rough Sets, Springer-Verlag (submitted)Google Scholar
  5. 5.
    Pawlak, Z.: Rough Sets - Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Dordrecht (1991)zbMATHGoogle Scholar
  6. 6.
    Skowron, A., Rauszer, C.: The discernibility matrices and functions in information systems. Intelligent Decision Support. In: Slowinski, R. (ed.) Handbook of Applications and Advances of the Rough Set Theory, pp. 331–362. Kluwer Academic Publishers, Dordrecht (1992)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Mikhail Ju. Moshkov
    • 1
    • 2
  1. 1.Faculty of Computing Mathematics and CyberneticsNizhny Novgorod State UniversityNizhny NovgorodRussia
  2. 2.Institute of Computer ScienceUniversity of SilesiaSosnowiecPoland

Personalised recommendations