Abstract: Mining high utility itemsets from a large transactional database refers to the discovery of knowledge like high utility itemsets (profits). Since a number of relevant algorithms have been proposed in past years, they fall into the problem of producing a large number of candidate itemsets for high utility itemsets. Such a huge number of candidate itemsets decrease the mining performance in terms of time and space complexity. The situation may become worse when the database contains lots of long transactions or long high utility itemsets. An emerging concept in the field of data mining is utility mining. To identify the itemsets with highest utilities is the main objective of utility mining, by considering profit, quantity, cost or other user preferences. This topic is having many applications in website click stream analysis, cross marketing in retail stores, business promotion in chain hypermarkets, online e-commerce management, finding important patterns in biomedical applications and mobile commerce environment planning.

Keywords: Candidate pruning; frequent itemset; high utility itemset; utility mining; data mining.