Improving the Accuracy and Efficiency of CBA Algorithm

Classification is an important research topic in data mining field, and it is one of main task of data mining. CBA (Classification Based on Associations) is a classification algorithm integration association rule mining and classification. CBA has been widely used in data mining areas because it has high classification accuracy and strong flexibility at handling unstructured data. However, when the samples become more and more large and characteristic attributes become more and more numerous, CBA algorithm becomes much lower. In this paper, an improved CBA algorithm based on rough set is proposed to improve both accuracy and efficiency. The improved CBA algorithm applies rough set theory to reduce attributes, and prune candidate rules with PEP method. Experimental result illustrate that the improved CBA algorithm is more efficient than CBA, and it has higher accuracy than CBA and C4.5. KeywordS: Data mining, CBA classification, Rough set, Attributes induction, PEP Received: Received 02 December 2009, Revised 03 February 2010, Accepted 9 February 2010 © 2009 D-Line. All rights reserved.