Mining Electronic Itemsets by Using EClaT Algorithm with Correlation Analysis

Abstract

Frequent itemsets mining is a discovery of interesting associations and correlations between itemsets in transcational and relational databases. Associaton rule mining is a popular method in the retail sales industry where a company is interested in identifying items that are frequently purchased together. The discovery of interesting correlation relationships among huge amounts of business transcation records can help in many business decision-making processes. In the proposed system, EClat algorithm based on verticla data format is used for mining frequent itemsets. This sytem analyzes the electronic items by finding correlation between the different items that sellers place in their shopping baskets. The implemented system can help user to choose and buy the interested electronic items easily without time consuming.