Abstract
Associative classification is a promising new approach that mainly uses association rule mining in classification. However, most associative classification approaches suffer from the huge number of the generated classification rules which takes efforts to select the best ones in order to construct the classifier. In this paper, a new associative classification approach called Garc is proposed. Garc takes advantage of generic basis of association rules in order to reduce the number of association rules without jeopardizing the classification accuracy. Furthermore, since rule ranking plays an important role in the classification task, Garc proposes two different strategies. The latter are based on some interestingness measures that arise from data mining literature. They are used in order to select the best rules during classification of new instances. A detailed description of this method is presented, as well as the experimentation study on 12 benchmark data sets proving that Garc is highly competitive in terms of accuracy in comparison with popular classification approaches.
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References
Zaïane, O.R., Antonie, M.-L.: On Pruning and Tuning Rules for Associative Classifiers. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds.) KES 2005. LNCS (LNAI), vol. 3683, pp. 966–973. Springer, Heidelberg (2005)
Xiaoxin Yin, J.H.: CPAR: Classification based on Predictive Association Rules. In: Proceedings of the SDM, San Francisco, CA, pp. 369–376 (2003)
Quinlan, J.R.: C4.5: Programs for Machine Learning (1993)
Stumme, G., Pasquier, N., Bastide, Y., Taouil, R., Lakhal, L.: Mining Minimal Non-redundant Association Rules Using Frequent Closed Itemsets. In: Palamidessi, C., Moniz Pereira, L., Lloyd, J.W., Dahl, V., Furbach, U., Kerber, M., Lau, K.-K., Sagiv, Y., Stuckey, P.J. (eds.) CL 2000. LNCS (LNAI), vol. 1861, Springer, Heidelberg (2000)
Gasmi, G., BenYahia, S., Nguifo, E.M., Slimani, Y.: \(\mathcal{IGB}\): A new informative generic base of association rules. In: Ho, T.-B., Cheung, D., Liu, H. (eds.) PAKDD 2005. LNCS (LNAI), vol. 3518, pp. 81–90. Springer, Heidelberg (2005)
Liu, B., Hsu, W., Ma, Y.: Integrating classification and association rule mining. In: Knowledge Discovery and Data Mining, pp. 80–86 (1998)
Li, W., Han, J., Pei, J.: CMAR: Accurate and efficient classification based on multiple class-association rules. In: Proceedings of IEEE International Conference on Data Mining (ICDM 2001), San Jose, CA, pp. 369–376. IEEE Computer Society, Los Alamitos (2001)
Antonie, M., Zaiane, O.: Text Document Categorization by Term Association. In: Proceedings of the IEEE International Conference on Data Mining (ICDM 2002), Maebashi City, Japan, pp. 19–26 (2002)
Antonie, M., Zaiane, O.: Classifying Text Documents by Associating Terms with Text Categories. In: Proceedings of the Thirteenth Austral-Asian Database Conference (ADC 2002), Melbourne, Australia (2002)
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Bocca, J.B., Jarke, M., Zaniolo, C. (eds.) Proceedings of the 20th Intl. Conference on Very Large Databases, Santiago, Chile, pp. 478–499 (1994)
Wang, J., Karypis, G.: HARMONY: Efficiently mining the best rules for classification. In: Proceedings of the International Conference of Data Mining, pp. 205–216 (2005)
Quinlan, J., Cameron-Jones, R.: FOIL: A midterm report. In: Proceedings of European Conference on Machine Learning, Vienna, Austria, pp. 3–20 (1993)
Bastide, Y.: Data mining: algorithmes par niveau, techniques d’implantation et applications. Phd thesis, Ecole Doctorale Sciences pour l’Ingénieur de Clermont-Ferrand, Université Blaise Pascal, France (2000)
Ganter, B., Wille, R.: Formal Concept Analysis. Springer, Heidelberg (1999)
Pasquier, N., Bastide, Y., Taouil, R., Lakhal, L.: Efficient Mining of Association Rules Using Closed Itemset Lattices. Journal of Information Systems 24, 25–46 (1999)
BenYahia, S., Nguifo, E.M.: Revisiting generic bases of association rules. In: Kambayashi, Y., Mohania, M., Wöß, W. (eds.) DaWaK 2004. LNCS, vol. 3181, pp. 58–67. Springer, Heidelberg (2004)
Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann, San Francisco (2001)
Lallich, S., Teytaud, O.: Evaluation et validation de l’intérêt des règles d’association. In: RNTI-E, pp. 193–217 (2004)
Bouzouita, I., Elloumi, S.: GARC-M: Generic association rules based classifier multi-parameterizable. In: Proceedings of 4th International Conference of the Concept Lattices and their Applications (CLA 2006), Hammamet, Tunisia (2006)
Hamrouni, T., BenYahia, S., Slimani, Y.: Prince: An algorithm for generating rule bases without closure computations. In: Tjoa, A.M., Trujillo, J. (eds.) DaWaK 2005. LNCS, vol. 3589, pp. 346–355. Springer, Heidelberg (2005)
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Bouzouita, I., Elloumi, S. (2008). Efficient Generic Association Rules Based Classifier Approach. In: Yahia, S.B., Nguifo, E.M., Belohlavek, R. (eds) Concept Lattices and Their Applications. CLA 2006. Lecture Notes in Computer Science(), vol 4923. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78921-5_11
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DOI: https://doi.org/10.1007/978-3-540-78921-5_11
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