Weighted association rule mining reflects semantic significance of item by considering its weight. Classification extracts set of rules and constructs a classifier to predict the new data instance. This paper proposes compact weighted associative classification method, which integrates weighted association rule mining and classification for constructing an efficient weighted associative classifier. Compact weighted associative classification algorithm randomly chooses one non class 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. In this proposed work, weight of item is computed by considering quality of the transaction using link based model. Experimental results show that the proposed system generates less number of high quality rules. © 2011 IEEE.