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

Virendra Kumar Shrivastava1, Parveen Kumar2 and K. R. Pardasani3

1Department of Computer Engineering, Singhania University, Pacheri Bari, (Rajsthan), India.

2Department of Computer Science & Engineering, Asia Pacific Institute of Information Technology, Panipat (Haryana), India.

3Dept. of Maths & MCA, Maulana Azad National Inst. Of Tech., Bhopal, (M. P.) India.


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.

Keywords: Data mining, Discovery of association rules, Multiple-level association rules, FP-tree, FP(l)-tree, COFI-tree, Concept hierarchy.

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