A CLASS OF ASSOCIATION RULE FOR PRE-ASSIGNED WEIGHTS

Abstract

Association rule mining aims to explore large transaction databases for association rules, which may reveal the implicit relationships among the data attributes. Weighted association rule mining reflects classification constructs the classifier and predicts the new data instance. Associative classification algorithm chooses one non class informative attribute from dataset and all the weighted class association rules are generated based on that attribute. The weight of the item is considered as one of the parameter in generating the weighted class association rules. This proposed algorithm calculates the weight using the HITS model. Comparative results show that the proposed system generates less number of high quality rules which improves the classification accuracy. It has turned into a thriving research topic in data mining and has numerous practical applications, including cross marketing, classification, text mining, Web log analysis, and recommendation systems.