AN EFFICIENT ALGORITHM FOR MINING HIGH UTILITY RARE ITEMSETS OVER UNCERTAIN DATABASES

In modern era, due to the broad applications of uncertain data, mining itemsets over uncertain databases has paying much more attention. Association Rule Mining (ARM) is a well known and most popular technique of Data Mining. It identifies itemsets from the dataset which appears frequently and generates association rules. This is the procedure which is followed by the traditional ARM it does not consider the utility of an itemsets. In real-world applications such as retail marketing, medical diagnosis, client segmentation etc., utility of itemsets is varied on various constraints such as based on cost, profit or revenue. Utility Mining intend to discover itemsets with their utilities by considering profit, quantity, cost or other user preferences.[22]High-utility itemset mining (HUIM) has thus emerged as an important research topic in data mining. But most HUIM algorithms only handle precise data, even though big data collected in reallife applications using experimental measurements or noisy sensors is often uncertain. High-Utility Rare Itemset (HURI) mining finds itemsets from a database which have their utility no less than a given minimum utility threshold and have their support less than a given frequency threshold. Identifying high-utility rare itemsets from a database can help in better business decision making by highlighting the rare itemsets which give high profits so that they can be marketed more to earn good profit. Koh and Rountree (2005) proposed a modified apriori inverse algorithm to generate rare itemsets of user interest. In this paper we propose an efficient algorithm named Mining High Utility Rare Itemsts over Uncertain Database (HURIU) .This novel approach uses the concept of apriori inverse over uncertain databases. This paper will also give the new version or extension of the algorithm HURI proposed by Jyothi et al. The implementation of an algorithm for the analysis is done on JDK 6.1 and referred the sample dataset presented by Lan Y.et al,2015[15] for uncertain database.