Abstract
Associative classification is recognized by its high accuracy and strong flexibility in managing unstructured data. However, the performance is still induced by low quality dataset which comprises of noised and distorted data during data collection. The noisy data affected support value of an itemset and so it influenced the performance of an associative classification. The performance of associative classification is relied on the classification where the classification is worked based on the class association rules which generated from frequent rule mining process. To optimize the frequent itemsets based on the support value, in this research, we proposed a new optimization pruning technique to prune decision tree according to the correlation of each decision tree branches using genetic algorithm.
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References
Thabtah, F., Cowling, P., Peng, Y.: Real performance of categorization-based association rule techniques (2005)
Agrawal, S., Pandey, N.K.: A comparison between two association rule mining techniques. Curr. Trends Inf. Technol. 1 (2012)
Tran, A., Truong, T., Le, B.: Structures of association rule set. In: Intelligent Information and Database Systems, pp. 361–370 (2012)
Agrawal, R., Imielinski, T.: Mining association rules between sets of items in large databases (1993)
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules, pp. 487–499 (1994)
Agrawal, A., Thakar, U., Soni, R., Chaurasia, B.K.: Efficiency enhanced association rule mining technique. In: Advances in Parallel Distributed Computing, pp. 375–384 (2011)
Vedula, V.R., Thatavarti, S.: Binary association rule mining using Bayesian network (2011)
Tran, A., Truong, T., Le, B.: Structures of association rule set. In: Intelligent Information and Database Systems, pp. 361–370 (2012)
Witten, I.H., Frank, E.: Data mining: practical machine learning tools and techniques with Java implementations. ACM SIGMOD Rec. 31, 76–77 (2002)
Liu, B., Hsu, W., Ma, Y.: Integrating classification and association rule mining. Appeared in KDD-98, New York (1998)
Wenmin, L., Jiawei, H., Jian, P.: CMAR: accurate and efficient classification based on multiple class-association rules, pp. 369–376 (2001)
Yin, X., Han, J.: CPAR: classification based on predictive association rules. Society for Industrial & Applied Mathematics, p. 331 (2003)
Chen, J., Wang, X., Zhai, J: Pruning decision tree using genetic algorithms, pp. 244–248. IEEE (2009)
Fürnkranz, J., Gamberger, D., Lavrač, N.: Pruning of rules and rule sets. In: Foundations of Rule Learning, pp. 187–216 (2012)
Coenen, F., Leng, P., Ahmed, S.: Data structure for association rule mining: T-trees and P-trees. IEEE Trans. Knowl. Data Eng. 16, 774–778 (2004)
Hawkins, D.M.: The problem of overfitting. J. Chem. Inf. Comput. Sci. 44, 1–12 (2004)
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Chern-Tong, H., Aziz, I.A. (2018). A Performance Evaluation of Chi-Square Pruning Techniques in Class Association Rules Optimization. In: Silhavy, R., Silhavy, P., Prokopova, Z. (eds) Applied Computational Intelligence and Mathematical Methods. CoMeSySo 2017. Advances in Intelligent Systems and Computing, vol 662. Springer, Cham. https://doi.org/10.1007/978-3-319-67621-0_18
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DOI: https://doi.org/10.1007/978-3-319-67621-0_18
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