Improved Prediction Based Mining Approach for Classification using Association rules - Mittal, K; Aggarwal, G; Mahajan, P (International Journal of Computer Sciences and Engineering, 2025)
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