WITHDRAWN: Mining of negative association rules using correlate frequent pattern tree

Abstract

Associative rule mining is the discovery of rules from the item set constraint that contain a set of specific items in antecedent rule and a class label. Frequent Pattern (FP-Tree) is associative rule mining method that doesn’t provide large set of candidate sets. Recently, the researchers are focusing on finding the infrequent item set leads to Negative Association Rule (NAR) mining. In this research, (FP-Tree) is added with the linkage table called Correlated Frequent Pattern Tree (CFPT) that allows the method to analyze the patterns with frequent items. The Coverage Co-efficient Measure (CCM) is applied in the FP-Tree with linkage table to identify the correlated item sets. In the mining process, CCM helps to remove irrelevant item set from the transaction. The developed IFP-Tree reduces the memory usage and the execution time in mining the frequent pattern. The four datasets have used to experiment the performance of proposed CFPT technique. The execution time of proposed CFPT method is the 0.043 s in frequent pattern mining and 0.075 s in infrequent pattern mining.