ANALYSIS OF RULE RANKING AND RULE PRUNING IN ASSOCIATIVE CLASSIFICATION TECHNIQUE

In Data Mining, Association rule mining and classification are most important tasks for decision making process based on data. Single-Label classification only Predict the single class label but nowadays most of the Application require more than one class label for Prediction eg. Music and text categorization, Medical diagnosis. Above Issue can be solved by Multi-label classification But the disadvantage of that is in this technique due to more number of label number of rules generated by the algorithm is too large and redundant. So discovery of strong rules is more harder this problem can be solved by the ranking and Pruning method using Parameter like Entropy and Information Gain. Still not giving good result so future work is we can apply pruning using the factors like Confidence and database Coverage. In this paper some of the techniques for rule ranking and pruning are explained for associative classification.