A new algorithm for mining frequent patterns in Can Tree

Association Rule Mining is concerned with the search for relationships between item-sets based on co-occurrence of patterns. Since transactional databases are being updated all the time and there are always data being added or deleted, so Incremental Association Rule Mining is very importance. Many methods have been presented so far for incremental frequent patterns mining, one of these methods is the frequent patterns mining base on the CanTree (CANonical-order TREE). Related works on CanTree, didn´t discuss about extraction of frequent patterns from the tree and it has only been suggested that the mining method would be similar to FP-growth. In this paper, a new method is presented for mining CanTree, and it is evaluated to show its improvement over the FP-growth method that mine FP tree. The evaluation results have demonstrated that performance of the presented algorithm is better than the FP-growth algorithm at high minimum support thresholds and for future work can try to improve it for lower minimum support threshold.