Efficient Algorithm for Mining High Utility Itemsets from Large Datasets Using Vertical Approach

Abstract

High Utility Itemset Mining is a challenging task as the Downward Closure Property present in frequent itemset mining does not hold here. In recent times many algorithms have been proposed for mining high utility itemsets ,but most of them follow a two-phase horizontal approach in which candidate itemsets are generated first and then the actual high utility itemsets are mined by performing another database scan. This approach generates a large number of candidate itemsets which are not actual high utility itemsets thus causing memory and time overhead to process them. To overcome this problem we propose a single phase algorithm which uses vertical database approach. Exhaustive search can mine all the high utility itemsets but it is expensive and time consuming. Two strategies based on u-list structure and item pair co-existence map are used in this algorithm for efficiently pruning the search space to avoid exhaustive search. Experimental analysis over various databases show that the proposed algorithm outperforms the two-phase algorithms UP-Growth, UP-Growth+ and IHUP in terms of running times and memory consumption.