Overview on Methods for Mining High Utility Itemset from Transactional Database

In this paper, finding itemsets with high utility like profits. Many algorithms have been proposed that having problem of producing a large number of candidate itemsets for high utility itemsets. Such a large number of candidate itemsets degrades the mining performance in terms of execution time and space requirement. This situation is difficult when the database contains lots of long transactions or long high utility itemsets. In this paper, we propose two algorithms, namely utility pattern growth (UP-Growth) and UP-Growth+, for mining high utility itemsets with a set of effective strategies for pruning candidate itemsets. Information of high utility itemsets maintained in Up-tree, candidate itemsets can be generated efficiently with only two scans of database. Experimental results show that the proposed algorithms, especially UP Growth+, not only reduce the number of candidates effectively but also outperform other algorithms substantially in terms of runtime, especially when databases contain lots of long transactions. Utility-based data mining is a new research area interested in all types of utility factors in data mining processes and targeted at incorporating utility considerations in both predictive and descriptive data mining tasks. High utility itemset mining is a research area of utility based descriptive data mining, aimed at finding itemsets that contribute most to the total utility.