Abstract
We show that the decision tree representation and the knowledge rules representation for data mining are semantically equivalent. Quinlan's production rule generators use attribute removal functions that are more powerful than ID3. The HCC-algorithm uses both attribute removal and concept tree ascension in its generalization function. The HCC-algorithm usually has more generalization power than the other ones. On computational efficiency, ID3 and the HCC-algorithm are efficient while Quinlan's production rule generators are less efficient. We also point out some disadvantages of the HCC-algorithm.
We propose a hybrid algorithm that combines all the above generalization functions and at the same time avoids over-generalization carefully. It is stable, complete, and non-local and is about as efficient as Quinlan's rule generators.
Preview
Unable to display preview. Download preview PDF.
References
R. Agrawal, T. Imielinski, and A. Swami. Mining association rules between sets of items in large databases. In Proc. 1993 ACM-SIGMOD, pages 207–216, May 1993.
T. G. Dietterich and R. S. Michalski. A comparative review of selected methods for learning from examples. In Michalski et al., editor, Machine Learning: An Artificial Intelligence Approach, Vol. 1, pages 41–82. Morgan Kaufmann, 1983.
J. Han, Y. Cai, and N. Gercone. Knowledge discovery in databases: An attributeoriented approach. In Proc. 18th VLDB, pages 547–559, August 1992.
R. S. Michalski. A theory and methodology of inductive learning. In Michalski et al., editor, Machine Learning: An Artificial Intelligence Approach, Vol. 1, pages 83–134. Morgan Kaufmann, 1983.
T. M. Mitchell. Generalization as search. Artificial Intelligence, 18:203–226, 1982.
G. Piatetsky-Shapiro. Discovery, analysis, and presentation of strong rules. In [7] pages 229–238. 1991.
G. Piatetsky-Shapiro and W. J. Frawley. Knowledge Discovery in Databases. AAAI/MIT Press, 1991.
J. R. Quinlan. Induction of decision trees. Machine Learning, 1:81–106, 1986.
J. R. Quinlan. Generating production rules from decision trees. In Proc. 10th Int. Joint Conf. Artificial Intelligence, pages 304–307, Milan, Italy, August 1987.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1996 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Han, J.L. (1996). Decision trees, knowledge rules and some related data mining algorithms. In: Jeffery, K.G., Král, J., Bartošek, M. (eds) SOFSEM'96: Theory and Practice of Informatics. SOFSEM 1996. Lecture Notes in Computer Science, vol 1175. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0037419
Download citation
DOI: https://doi.org/10.1007/BFb0037419
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-61994-9
Online ISBN: 978-3-540-49588-8
eBook Packages: Springer Book Archive