An Automated Support Threshold Based on Apriori Algorithm for Frequent Itemsets

Data mining is the technique to extract features from raw data. In Today’s era data mining has a lot of e-Commerce applications. It is widely used in a variety of application areas like banking, marketing and retail industry. The association rules generated from them are still important items. Apriori based algorithms tend to achieve high efficiency; when the database transactions are scarce.Study proposes an approach to deal with frequent item problem. Main goal is to provide an algorithm for frequent itemset mining with automated support thresholds. Apriori follows breadth search and bottom up approaches. It enumerates all frequent items with some modifications. It only checks the items only when it is existed in database for making more frequent itemset. It reduces the time complexity as well as space complexity with the more frequent outcome of itemsets.