No abstract

Data Mining can be defined as an activity that extracts some new nontrivial information contained in large databases. Traditional data mining techniques have focused largely on detecting the statistical correlations between the items that are more frequent in the transaction databases. Also termed as frequent itemset mining, these techniques were based on the rationale that itemsets which appear more frequently must be of more importance to the user from the business perspective .In this thesis we throw light upon an emerging area called Utility Mining which not only considers the frequency of the itemsets but also considers the utility associated with the itemsets. The term utility refers to the importance or the usefulness of the appearance of the itemset in transactions quantified in terms like profit, sales or any other user preferences. In High Utility Itemset Mining the objective is to identify itemsets that have utility values above a given utility threshold. In existing system some high utility itemset mining algorithms such as Two-Phase, UPGrowth have been proposed. But there is problem like it requires more execution time and it uses more memory. The new method is memory efficient technique for mining high utility itemsets from transactional databases. This technique requires less memory space and execution time than existing algorithms.