Abstract
Mining association rules at multiple concept levels leads to the discovery of more concrete knowledge. Nevertheless, setting an appropriate minsup for cross-level itemsets is still a non-trivial task. Besides, the mining process is computationally expensive and produces many redundant rules. In this work, we propose an efficient algorithm for mining generalized association rules with multiple minsup. The method scans appropriately k+1 times of the number of original transactions and once of the extended transactions where k is the largest itemset size. Encouraging simulation results were reported.
Ack: supported by the H.K. Polytechnic University Research Studentship, project no. G-V722
Chapter PDF
Similar content being viewed by others
Keywords
- Association Rule
- Mining Association Rule
- Conceptual Distance
- Artificial Intelligence Approach
- Generalize Association Rule
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.
Reference
R. Agrawal and R. Srikant, “Fast algorithms for mining association rules” Proc. VLDB Conference, Santiago, Chile, pp.487–499, Sept. 1994.
J. Han and Y. Fu, “Discovery of multiple-level association rules from large databases”, Proc. 21st VLDB Conference, Zurich, Switzerland, pp.420–431, Sept. 1995.
J. Han and Y. Fu, “Mining multiple-level association rules in large databases”, IEEE Trans. on Knowledge and Data Engineering, Vol.11, No.5, pp.798–805, Sept. 1999.
M. Klemettinen, et. al., “Finding interesting rules from large sets of discovered association rules”, Proc. 3rd Int. Conf. on Information and Knowledge Management, pp.401–408, 1994.
Y. Kodratoff, and J. G. Ganascia, “Improving the generalization step in learning”, Machine Learning: An Artificial Intelligence Approach, vol.2, LA, pp. 215–244, 1986.
Y. Kodratoff, and G. Tecuci, “Learning based on conceptual distance”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.10, No.6, pp.897–909, Nov. 1988.
B. Liu, W. Hsu, and Y. Ma, “Mining association rules with multiple minimum supports”, Proc. ACM SIGKDD Conf. on KDD, San Diego, CA, pp. 337–341, August, 1999.
R. S. Michalski, and R. Stepp, “Learning by observation”, Machine Learning: An Artificial Intelligence Approach, pp.163–190, 1983.
R. Srikant and R. Agrawal, “Mining generalized association rules”, Proc. of the 21st VLDB Conf., Zurich, Switzerland, pp.407–419, Sept. 1995.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lui, CL., Chung, FL. (2000). Discovery of Generalized Association Rules with Multiple Minimum Supports. In: Zighed, D.A., Komorowski, J., Żytkow, J. (eds) Principles of Data Mining and Knowledge Discovery. PKDD 2000. Lecture Notes in Computer Science(), vol 1910. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45372-5_59
Download citation
DOI: https://doi.org/10.1007/3-540-45372-5_59
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-41066-9
Online ISBN: 978-3-540-45372-7
eBook Packages: Springer Book Archive