Frequent Pattern Mining Using Modified CP-Tree for Knowledge Discovery

Mining frequent pattern from databases is useful for knowledge discovery. In this paper, we propose modified CP-Tree, which scans entire transactions only once and constructs the tree by inserting the transactions one by one. The constructed tree consists of an item list along with its occurrence. In addition, a sorted order of items with its frequency of occurrence is maintained and based on the sorted value, the tree is dynamically rearranged. In rearranging phase, the nodes are rearranged in each branch based on sorted order of items. Each path of the branch is removed from the tree, sorted based on sorted order of items and inserted back as a branch into the tree. We have evaluated the performance of the proposed modified tree on benchmark databases such as CHESS, MUSHROOM and T10I4D100K. It is observed that the time taken for extracting frequent item from the tree is encouraging compared to conventional CP-Tree.