Abstract
In this paper, we propose a novel satisfaction mechanism, named “Dynamic K”, which could be introduced in any Class Association Rules (CAR) based classifier, to determine the class of unseen transactions. Experiments over several datasets show that the new satisfaction mechanism has better performance than the main satisfaction mechanism reported (“Best Rule”, “Best K Rules” and “All Rules”). Additionally, the experiments show that “Dynamic K” obtains the best results independent of the CAR-based classifier used.
Chapter PDF
Similar content being viewed by others
References
Clark, P., Boswell, R.: Rule Induction with CN2: Some Recent Improvments. In: Proc. of European Working Session on Learning, pp. 151–163 (1991)
Liu, B., Hsu, W., Ma, Y.: Integrating classification and association rule mining. In: Proc. of the KDD, pp. 80–86 (1998)
Li, W., Han, J., Pei, J.: CMAR: Accurate and efficient classification based on multiple class-association rules. In: Proc. of the ICDM, pp. 369–376 (2001)
Coenen, F.: The LUCS-KDD discretised/normalised ARM and CARM Data Library (2003), http://www.csc.liv.ac.uk/~frans/KDD/Software/LUCS-KDD-DN
Yin, X., Han, J.: CPAR: Classification based on Predictive Association Rules. In: Proc. of the SIAM International Conference on Data Mining, pp. 331–335 (2003)
Wang, J., Karypis, G.: HARMONY: Efficiently mining the best rules for classification. In: Proc. of SDM, pp. 205–216 (2005)
Coenen, F., Leng, P., Zhang, L.: Threshold Tuning for Improved Classification Association Rule Mining. In: Ho, T.-B., Cheung, D., Liu, H. (eds.) PAKDD 2005. LNCS (LNAI), vol. 3518, pp. 216–225. Springer, Heidelberg (2005)
Demšar, J.: Statistical Comparisons of Classifiers over Multiple Data Sets. J. Mach. Learn. Res. 7, 1–30 (2006)
Steinbach, M., Kumar, V.: Generalizing the notion of confidence. Knowl. Inf. Syst. 12(3), 279–299 (2007)
Asuncion, A., Newman, D.J.: UCI Machine Learning Repository (2007), http://www.ics.uci.edu/~mlearn/MLRepository.html
Wang, Y.J., Xin, Q., Coenen, F.: A Novel Rule Weighting Approach in Classification Association Rule Mining. In: International Conference on Data Mining Workshops, pp. 271–276 (2007)
Wang, Y.J., Xin, Q., Coenen, F.: A Novel Rule Ordering Approach in Classification Association Rule Mining. In: Perner, P. (ed.) MLDM 2007. LNCS (LNAI), vol. 4571, pp. 339–348. Springer, Heidelberg (2007)
Cheng, H., Yan, X., Han, J., Philip, S.Y.: Direct Discriminative Pattern Mining for Effective Classification. In: Proc. of the ICDE, pp. 169–178 (2008)
Wang, Y.J., Xin, Q., Coenen, F.: Hybrid Rule Ordering in Classification Association Rule Mining. Trans. MLDM 1(1), 1–15 (2008)
Karabatak, M., Ince, M.C.: An expert system for detection of breast cancer based on association rules and neural network. Expert Syst. Appl. 36, 3465–3469 (2009)
Park, S.H., Reyes, J.A., Gilbert, D.R., Kim, J.W., Kim, S.: Prediction of protein-protein interaction types using association rule based classification. BMC Bioinformatics 10(1) (2009)
Bae, J.K., Kim, J.: Integration of heterogeneous models to predict consumer behavior. Expert Syst. Appl. 37, 1821–1826 (2010)
Hernández, R., Carrasco, J.A., Martínez, F.J., Hernández, J.: Classifying using Specific Rules with High Confidence. In: Proc. of the MICAI, pp. 75–80 (2010)
Malik, W.A., Unwin, A.: Automated error detection using association rules. Intelligent Data Analysis 15(5), 749–761 (2011)
Hernández, R., Carrasco, J.A., Martínez, F.J., Hernández, J.: CAR-NF: A Classifier based on Specific Rules with High Netconf. Intelligent Data Analysis 16(1), 49–68 (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hernández-León, R. (2013). Dynamic K: A Novel Satisfaction Mechanism for CAR-Based Classifiers. In: Ruiz-Shulcloper, J., Sanniti di Baja, G. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2013. Lecture Notes in Computer Science, vol 8258. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41822-8_18
Download citation
DOI: https://doi.org/10.1007/978-3-642-41822-8_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-41821-1
Online ISBN: 978-3-642-41822-8
eBook Packages: Computer ScienceComputer Science (R0)