Algorithmic Framework for Frequent Pattern Mining with FP-Tree

Abstract

The FP-tree algorithm is currently one of the fastest approaches to frequent item set mining. Studied have also shown that pattern-growth method is one of the most efficient methods for frequent pattern mining. it is based on a prefix tree representation of the given data base of transaction (FP-tree) and can save substantial amount of memory for storing the database. The basic idea of the FP-growth algorithm can be described as a recursive elimination scheme which is usually achieved in the processing step by deleting all items from the transactions that are not frequent. In this study, a simple framework for mining frequent pattern is presented with FP-tree structure which is an extended prefix-tree structure for mining frequent pattern without candidate generation, and less cost for better understanding of the concept for inexperienced data analysts and other organizations interested in association rule mining