Abstract
Associative classifiers have been the subject of intense research for the last few years. Experiments have shown that they generally result in higher accuracy than decision tree classifiers. In this paper, we introduce a novel algorithm for associative classification “Classification based on Association Rules Generated in a Bidirectional Apporach” (CARGBA). It generates rules in two steps. At first, it generates a set of high confidence rules of smaller length with support pruning and then augments this set with some high confidence rules of higher length with support below minimum support. Experiments on 6 datasets show that our approach achieves better accuracy than other state-of-the-art associative classification algorithms.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Francisco (1993)
Duda, R., Hart, P.: Pattern Classification and Scene Analysis. John Wiley & Sons, Chichester (1973)
Lim, T.S., Loh, W.Y., Shih, Y.S.: A comparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithms. Machine Learning 39 (2000)
Cristianini, N., Shawe-Taylor: An Introduction to Support Vector Machines. Cambridge University Press, Cambridge (2000)
Liu, B., Hsu, W., Ma, Y.: CBA: Integrating Classification and Association Rule Mining. In: KDD 1998, New York, NY (August 1998)
Dong, G., Zhang, X., Wong, L., Li, J.: Caep: Classification by Aggregating Emerging Patterns. In: Arikawa, S., Furukawa, K. (eds.) DS 1999. LNCS (LNAI), vol. 1721, Springer, Heidelberg (1999)
Li, W., Han, J., Pei, J.: CMAR: Accurate and efficient classification based on multiple class-association rules. In: ICDM 2001, San Jose, CA (November 2001)
Agrawal, R., Imielinski, T., Swami, A.: Mining Association Rules between Sets of Items in Large Databases. In: Proc. Of the SIGMOD, Washington, D.C., pp. 207–216 (1993)
Blake, C., Merz, C.: UCI repository of machine learning databases, http://www.ics.uci.edu/~mlearn/MLRepository.html
Antonie, M., Zaïane, O.R.: An Associative Classifier based on Positive and Negative Rules. In: DMKD 2004, Paris, France, June 13 (2004)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kundu, G., Munir, S., Bari, M.F., Islam, M.M., Murase, K. (2008). A Novel Algorithm for Associative Classification. In: Ishikawa, M., Doya, K., Miyamoto, H., Yamakawa, T. (eds) Neural Information Processing. ICONIP 2007. Lecture Notes in Computer Science, vol 4985. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69162-4_47
Download citation
DOI: https://doi.org/10.1007/978-3-540-69162-4_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69159-4
Online ISBN: 978-3-540-69162-4
eBook Packages: Computer ScienceComputer Science (R0)