The art of data mining has been constantly evolving. There are a number of innovative and intuitive techniques that have emerged that fine-tune data mining concepts in a bid to give companies more comprehensive insight into their own data with useful future trends. Businesses can use data mining for knowledge discovery and exploration of available data. This can help them predict future trends, understand customer’s preferences and purchase habits, and conduct a constructive market analysis. They can then build models based on historical data patterns and garner more from targeted market campaigns as well as strategize more profitable selling approaches. Data mining helps enterprises to make informed business decisions, enhances business intelligence, thereby improving the company’s revenue and reducing cost overheads. We consider the problem of discovering association rules between items in a large database of sales transactions. We proposed a new algorithm for solving this problem that is fundamentally based on and improved upon Apriori Algorithm and its other updated versions. The proposed method is to find out all the frequent Item sets without scanning DB so many times. Count the support of the frequent itemsets by scanning the DB another time; Output the association rules from the frequent itemsets. The proposed algorithm in this paper reduces the times of scanning DB.
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