Abstract
Associative classification is a promising approach in data mining, which integrates association rule discovery and classification. In this paper, we present a novel associative classification technique called Ranked Multilabel Rule (RMR) that derives rules with multiple class labels. Rules derived by current associative classification algorithms overlap in their training data records, resulting in many redundant and useless rules. However, RMR removes the overlapping between rules using a pruning heuristic and ensures that rules in the final classifier do not share training records, resulting in more accurate classifiers. Experimental results obtained on twenty data sets show that the classifiers produced by RMR are highly competitive if compared with those generated by decision trees and other popular associative techniques such as CBA, with respect to prediction accuracy.
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References
Agrawal, R., & Srikant, R. Fast algorithms for mining association rule. Proceedings of the 20th International Conference on Very Large Data Bases, (pp. 487–499), 1994.
Baralis, E., & Torino, P. A lazy approach to pruning classification rules. Proceedings of the 2002 IEEE International Conference on Data Mining, (pp. 35), 2002.
Cohen, W. Fast effective rule induction. Proceedings of the 12th International Conference on Machine Learning, (pp. 115–123). Morgan Kaufmann, CA, 1995.
Cowling P., & Chakhlevitch, K. Hyperheuristics for Managing a Large Collection of Low Level Heuristics to Schedule Personnel. Proceedings of 2003 IEEE conference on Evolutionary Computation, Canberra, Australia, 8–12 Dec, (pp. 1214–1221), 2003.
Fayyad, U., & Irani, K. Multi—interval discretisation of continues-valued attributes for classification learning. Proceeding of IJCAI, (pp. 1022–1027), 1993.
Li, W., Han, J., & Pei, J. CMAR: Accurate and efficient classification based on multiple-class association rule. Proceedings of the ICDM’01, (pp. 369–376). San Jose, CA, 2001.
Liu, B., Hsu, W., & Ma, Y. Integrating classification and association rule mining. Proceeding of the KDD, (pp. 80–86). New York, NY, 1998.
Quinlan, J. C4.5: Programs for Machine Learning. San Mateo, CA: Morgan Kaufmann, 1993.
Quinlan, J., & Cameron-Jones, R. FOIL: A midterm report. Proceedings of European Conference on Machine Learning, (pp. 3–20). Vienna, Austria, 1993.
Thabtah, F. Rule Preference Effect in Associative Classification Mining. Journal of Information and Knowledge Management, Vol 5(1): 1–7, 2006.
Thabtah, F., Cowling, P., & Peng, Y. MCAR: Multi-class Classification based on Association Rule Approach. Proceeding of the 3 rd IEEE International Conference on Computer Systems and Applications (pp. 1–7). Cairo, Egypt, 2005.
Thabtah, F., Cowling, P., & Peng, Y. A New Multi-class, Multi-label Associative Classification Approach. Proceeding of the 4th International Conference on Data Mining (pp. 217–224). Brighton, UK, 2004.
Witten, I., & Frank, E. Data mining: practical Machine learning tools and techniques with Java implementations. San Francisco: Morgan Kaufmann, 2000.
Yin, X., & Han, J. CPAR: Classification based on predictive association rule. Proceedings of the SDM (pp. 369–376). San Francisco, CA, 2003.
Zaki, M. & Gouda K. Fast Vertical Mining using Diffsets. Proceedings of the ninth ACMSIGKDD international conference on Knowledge discovery and data mining, (pp. 326–335). Washington, D.C, 2003.
CBA. http://www.comp.nus.edu.sg/~dm2/p_download.html, 1998.
WEKA. Data Mining Software in Java: http://www.cs.waikato.ac.nz/ml/weka, 2001.
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Thabtah, F. (2007). Ranked Multi-Label Rules Associative Classifier. In: Bramer, M., Coenen, F., Tuson, A. (eds) Research and Development in Intelligent Systems XXIII. SGAI 2006. Springer, London. https://doi.org/10.1007/978-1-84628-663-6_7
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DOI: https://doi.org/10.1007/978-1-84628-663-6_7
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