Basic Framework of CATSIM Tree for Efficient Frequent Pattern Mining

Abstract

Finding frequent patterns from databases have been the most time consuming process in association rule mining. Several effective data structures, such as two-dimensional arrays, graphs, trees and tries have been proposed to collect candidate itemsets and frequent itemsets. It seems that the tree structure is most extractive to storing itemsets. The outstanding tree has been proposed so far is called FP-tree which is a prefix tree structure. Some advancement with this tree structure is called CATS tree. CATS Tree extends an idea of FP-Tree to improve storage compression and allow frequent pattern mining without generation of candidate itemsets. It allows the mining with a single pass over the database. In this work, CATSIM Tree is presented for which an attempt has been made to modify present CATS Tree in order to make it efficient for incremental mining.