Abstract
Associative classification has aroused significant attention in recent years. This paper proposed a novel interestingness measure, named dilated chi-square, to statistically reveal the interdependence between the antecedents and the consequent of classification rules. Using dilated chi-square, instead of confidence, as the primary ranking criterion for rules under the framework of popular CBA algorithm, the adapted algorithm presented in this paper can empirically generate more accurate and much more compact decision lists.
Chapter PDF
Similar content being viewed by others
References
B. Liu, W. Hsu, and Y. Ma. Integrating Classification and Association Rule Mining. in the 4th International Conference on Discovery and Data Mining. 1998. New York, U.S.: pp. 80–86.
G. Dong, et al. CAEP: Classification by aggregating emerging patterns. in 2nd International Conference on Discovery Science, (DS’99), volume 1721 of Lecture Notes in Artificial Intelligence. 1999. Tokyo, Japan: Springer-Verlag: pp. 30–42.
W. Liu, J. Han, and J. Pei. CMAR: Accurate and efficient classification based on multiple class-association rules. in ICDM’01. 2001. San Jose, CA: pp. 369–376.
X. Yin and J. Han. CPAR: Classification based on predictive association rules, in 2003 SIAM International Conference on Data Mining (SDM’03). 2003. San Fransisco, CA: pp. 331–335.
K. Wang and S. Zhou. Growing decision trees on support-less association rules. in KDD’00. 2000. Boston, MA: pp. 265–269.
R. Agrawal and R. Srikant. Fast algorithm for mining association rules. in the 20th International Conference on Very Large Data Bases. 1994. Santiago, Chile: pp. 487–499.
Janssens, D., et al. Adapting the CBA-algorithm by means of intensity of implication. in the First International Conference on Fuzzy Information Processing Theories and Applications. 2003. Beijing, China: pp. 397–403.
C.L. Blake and C.J. Merz, UCI repository of machine learning databases. 1998, Irvine, CA: University of California, Dept. of Information and Computer Science. http://www.ics.uci.edu/~mlearn/mlrepository.htm.
J.R. Quinlan, C4.5 programs for machine learning. 1993: Morgan Kaufmann.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 International Federation for Information Processing
About this paper
Cite this paper
Lan, Y., Chen, G., Janssens, D., Wets, G. (2005). Dilated Chi-Square: A Novel Interestingness Measure to Build Accurate and Compact Decision List. In: Shi, Z., He, Q. (eds) Intelligent Information Processing II. IIP 2004. IFIP International Federation for Information Processing, vol 163. Springer, Boston, MA. https://doi.org/10.1007/0-387-23152-8_30
Download citation
DOI: https://doi.org/10.1007/0-387-23152-8_30
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-23151-8
Online ISBN: 978-0-387-23152-5
eBook Packages: Computer ScienceComputer Science (R0)