Agent based data classification approach for data mining

Abstract

Classification is one of the tasks in data mining. The form of classifier depends on the classification technique used. For example, neural network produce a set of weight as a classifier, regression form an equation as a predictor while decision tree, C4.5, CART, Rough Set and Bayesian theory generate set of rules known as rule based classifier. Rules are more interpretable by human when compared to other form of classifiers. The process of classification involves applying the rules onto a set of unseen data. There are many issues appeared in rule application process such as more than one rule match, multiple scanning of large rule base and uncertainty. In this study an agent based approach is proposed to improve the rule application process. The proposed agents are embedded within the standard rule application techniques. The result shows the significant improvements in classification time and the number of matched rules with comparable classification accuracy.