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

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.

Association Rules Mining in Big Data

“…One example of serial implementation is the well-known statistical computation with the package R, called "arules". Parallel implementation of the FP-Groth program is available in the library for studying the open source computer (MLlib) Apache Spark and Apache Mahout [18,19].…”