Discovery of Multi-level Association Rules from Primitive Level Frequent Patterns Tree

The problem of discovering association rules at single level has received significant research attention and several algorithms for mining frequent itemsets have been developed. Previous studies in data mining have yielded efficient algorithms for discovering association rules. However, discovery of association rules at multiple concept levels may lead to the mining of more specific and concrete knowledge from datasets. The discovery of multilevel association rules is very much useful in many applications. In most of the studies for multi-level association rule mining, the database is scanned repeatedly which affects the efficiency of mining process. In this research paper, a method for discovery of multi-level association rules from primitive level FP-tree is proposed in order to reduce main memory usage and make execution faster.