High Utility Itemset Mining from TransactionDatabase Using UP-Growth and UP-Growth+Algorithm

Abstract

Efficient discovery of itemsets with high utility like profits deals with the mining high utility itemsets from a transaction database Although a number of relevant approaches have been proposed in recent years, these algorithm incur the problem of producing a large number of candidate itemsets for high utility itemsets and probably degrades the mining performance in terms of execution time and memory space. In this paper, we propose two algorithms, viz., utility pattern growth (UP-Growth) and Improved UP-Growth i.e. Improved Utility Pattern Growth, for mining high utility itemsets with a set of effective strategies for pruning candidate itemsets. The information of high utility itemsets is maintained in a compact tree-based data structure utility pattern tree (UP-Tree), it scan the original database twice to manage data structured way. Proposed algorithms, especially Improved UP Growth, not only reduce the number of candidates effectively but also outperform other algorithms substantially in terms of runtime and memory consumption, especially when databases contain lots of long transactions .