Abstract
Associative classification aims to discover a set of constrained association rules, called Class Association Rules (CARs). The consequent of a CAR is a singleton and is restricted to be a class label. Traditionally, the classifier is built by selecting a subset of CARs based on some interestingness measure.
The proposed approach for associative classification, called Associative Classifier based on Closed Itemsets (ACCI), scans the dataset only once and generates a set of CARs based on closed itemsets (ClosedCARs) using a lattice based data structure. Subsequently, rule conflicts are removed and a subset of non-conflicting ClosedCARs which covers the entire training set is chosen as a classifier. The entire process is independent of the interestingness measure. Experimental results on benchmark datasets from UCI machine repository reveal that the achieved classifiers are more accurate than those built using existing approaches.
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References
Agarwal, R., Srikant, R.: Fast Algorithms for Mining Association Rules. In: 20th International Conference on Very Large Databases, pp. 487–499 (1994)
Antonie, M.L., Zaiane, O.R.: An Associative Classifier Based on Positive and Negative Rules. In: Proceedings of the 9th SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, pp. 64–69. ACM Press (2004)
Coenen, F., Leng, P.: Obtaining Best Parameter Values for Accurate Classification. In: Proceedings of 5th International Conference on Data Mining, pp. 549–552 (2005)
Deng, Z., Zheng, X.: Building Accurate Associative Classifier Based on Closed Itemsets and Certainty Factor. In: IEEE Third International Symposium on Intelligent Information Technology Application Workshop, pp. 141–144 (2009)
Gupta, A., Bhatnagar, V., Kumar, N.: Mining Closed Itemsets in Data Stream Using Formal Concept Analysis. In: Bach Pedersen, T., Mohania, M.K., Tjoa, A.M. (eds.) DAWAK 2010. LNCS, vol. 6263, pp. 285–296. Springer, Heidelberg (2010)
Heravi, M.J., Zaiane, O.R.: A Study on Interestingness Measures for Associative Classifiers. In: ACM Symposium on Applied Computing (2010)
Lan, Y., Chen, G., Wets, G.: Improving Associative Classification by Incorporating Novel Interestingness Measures. In: IEEE International Conference on e-Business Engineering (2005)
Li, W., Han, J., Pei, J.: CMAR: Accurate and Efficient Classification Based on Multiple Class Association Rules. In: Proceedings of the International Conference on Data Mining, pp. 369–376 (2001)
Liu, B., Hsu, W., Ma, Y.: Integrating Classification and Association Rule Mining. In: Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining (1998)
Pasquier, N., Bastide, Y., Taouil, R., Lakhal, L.: Efficient Mining of Association Rules using Closed Itemset Lattices. Journal of Information Systems 24(1), 25–46 (1999)
Sun, Y., Wong, K.C., Wang, Y.: An Overview of Associative Classifiers. In: Proceedings of the International Conference on Data Mining, DMIN, pp. 138–143 (2006)
Thabtah, F.: A Review of Associative Classification Mining. The Knowledge Engineering Review 22, 37–65 (2007)
Quinlan, J.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo (1993)
Yin, X., Han, J.: CPAR: Classification Based on Predictive Association Rules. In: Proceedings of SIAM Conference on Data Mining, pp. 331–335 (2003)
Zaki, M.J.: Generating Non-Redundant Association Rules. In: 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 34–43 (2000)
Zhang, X., Chen, G., Wei, Q.: Building a Highly-compact and Accurate Associative Classifier. Journal of Applied Intelligence 34, 74–86 (2011)
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Kumar, N., Gupta, A., Bhatnagar, V. (2012). Lattice Based Associative Classifier. In: Pan, JS., Chen, SM., Nguyen, N.T. (eds) Intelligent Information and Database Systems. ACIIDS 2012. Lecture Notes in Computer Science(), vol 7197. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28490-8_3
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DOI: https://doi.org/10.1007/978-3-642-28490-8_3
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