Efficient Approach for Mining High Utility Itemsets From Transactional Databases

Abstract

Mining high utility item sets from transactional database refers to the discovery of item sets with high utility like profits. Although a number of relevant algorithms have been proposed in recent years, they incur the problem of producing a large number of candidate item sets for high utility item sets. Such a large number of candidate item sets degrades the mining performance in terms of execution time and space requirement. The situation may become worse when the database contains lots of long transactions or long high utility item sets. An algorithm, namely UP-Growth proposed [7] for mining high utility item sets with a set of effective strategies for pruning candidate item sets. The information of high utility item sets is maintained in a tree-based data structure named UP-Tree such that candidate item sets can be generated efficiently with only two scans of database. Experimental results show that the proposed algorithm, 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 more no of transactions.