FP-tree and COFI Based Approach for Mining of Multiple Level Association Rules in Large Databases

TL;DR

This paper proposes a new efficient method for mining multi-level association rules in large databases using FP-tree and co-occurrence frequent item trees, reducing repeated database scans and improving knowledge discovery.

Contribution

It introduces a novel approach combining FP-tree and co-occurrence trees for multi-level rule mining, enhancing efficiency over existing methods.

Findings

Reduces the number of database scans needed for multi-level rule mining

Effectively discovers specific and concrete knowledge from large datasets

Improves efficiency of multi-level association rule mining

Abstract

In recent years, discovery of association rules among itemsets in a large database has been described as an important database-mining problem. The problem of discovering association rules has received considerable research attention and several algorithms for mining frequent itemsets have been developed. Many algorithms have been proposed to discover rules at single concept level. However, mining association rules at multiple concept levels may lead to the discovery of more specific and concrete knowledge from data. The discovery of multiple level association rules is very much useful in many applications. In most of the studies for multiple level association rule mining, the database is scanned repeatedly which affects the efficiency of mining process. In this research paper, a new method for discovering multilevel association rules is proposed. It is based on FP-tree structure and uses cooccurrence frequent item tree to find frequent items in multilevel concept hierarchy.