Classification is one of the important data mining applications in the areas of decision sciences and knowledge extraction from the data. Classification using Association Rule Mining(ARM) is in great demand today with an aim of building moderate sized classifier consisting of limited number of rules from the database with higher classification accuracy rate. This classification approach integrates two important data mining strategies ARM and classification. Association rule mining aims to discover rules without any target based on association among the frequent items in the data where as classification based rule mining aims to discover targeted rules towards a predetermined class. The integration of these two techniques focuses on mining a set of Association Rules (CARs) which is a subset of association rules generate by some ARM technique. This integration also helps to resolve few problems associated with traditional classification systems. This paper attempts to improve the performance of CBA classifier with some modifications and performs the experimental evaluation against traditional classifier C4.5 in terms of error rate, number of classification association rules generated and the execution time.

Rule mining, Classification Association rules, Classifier, Rule Pruning, CBA, Discretization.

[1]. Liu, B., Hsu, W., Ma, Y. CBA: Integrating Classification and Association Rule Mining. In KDD’98, New York, NY, Aug. 1998.
[2]. Agrawal,R. and Srikant, R., Fast algorithms for mining association rules, in Proc. 20th Int. Conf. Very Large Data Bases, VLDB, edited by J.B. Bocca, M. Jarke, and C. Zaniolo, Morgan Kaufmann 12 (1994) 487-499.
[3]. Kurgan. L and Cios, K.J.: Discretization Algorithm that Uses Class-Attribute Interdependence Maximization, Proceedings of the 2001 International Conference on Artificial Intelligence (IC-AI 2001), pp.980-987, Las Vegas, Nevada. (Pub. 2001.)
[4]. PDF) Data Mining Discretization Methods and Performances. Available from: https://www.researchgate.net/publication/264886861_Data_Mining_Discretization_Methods_and_Performances [accessed Jul 03 2018].
[5]. AL-Zawaidah, Farah Hanna., Jbara ,Yosef Hasan, “An Improved Algorithm for Mining Association Rules in Large Databases”, World of Computer Science and Information Technology Journal (WCSIT), ISSN: 2221-0741 Vol. 1, No. 7, 311-316, 2011. 7.
[6]. Agrawal R., Imielinski T. and Swami A. (1993) Mining Association rules between sets of items in large databases, In the Proc. of the ACM SIGMOD International Conf. on Management of Data (ACM SIGMOD , 93), Washington, USA, 207-216.
[7]. Hassan M. Najadat, Mohammed Al-Maolegi, Bassam Arkok, “An Improved Apriori Algorithm for Association Rules”, International Research Journal of Computer Science and Application Vol. 1, No. 1, June 2013, PP: 01 – 08.
[8]. Mahta, M., Agrawal, R. and Rissanen, J. 1996. SLIQ: A fast scalable classifier for data mining. Proc. of the fifth Int’l Conference on Extending Database Technology.
[9]. Antonie, M. . Za¨ıane ,O. R. An Associative Classifier based on Positive and Negative Rules. In DMKD ’04 Paris, France, June 13, 2004,
[10]. S.Rani, S.Kaushik, “Application of Data Mining techniques for predicting Diabetes” in International Journal of Scientific Research in Computer Science, Engineering and Information Technology, Vol 3(3),pp-1996-2004, 2018.ISSN-2456-3307.
[11]. A.Sharma, N.K.Tiwari. “ Mining Association Rules in Cloud Computing Environments using Modified Apriori” in International Journal of Scientific Research in Computer Science, Engineering and Information Technology, Vol 3(1),pp-629-634, 2018.ISSN-2456-3307.
[12]. http://cgi.csc.liv.ac.uk/~frans/KDD/Software/CBA/cba.html
[13]. ANALYTICS VIDHYA CONTENT TEAM, Mining frequent items bought together using Apriori Algorithm (with code in R) AUGUST 11, 2017 https://www.analyticsvidhya.com/blog/2017/08/mining-frequent-items-using-apriori-algorithm/
[14]. Merz, C. J. & Murphy, P. (1996). UCI Repository of Machine Learning Database. Available: http://www. ics.uci.edu/~mlearn/MLRepository