UP-GNIV: an expeditious high utility pattern mining algorithm for itemsets with negative utility values

Abstract

Traditionally, frequent pattern mining dealt in extracting frequency pattern from transaction databases by not considering utility factors. Utility-based data mining focuses on all aspects of economic utility in data mining and is aimed at incorporating utility in both predictive and descriptive data mining tasks. High utility itemset (HUI) mining process incurs the problem of producing a large number of candidate itemsets since downward closure property used in frequent itemset mining is not applied in utility mining and itemsets associated with negative utility values are not supported by existing algorithms. Here, mining high utility itemset with negative item values using Utility Pattern-Growth approach for Negative Item Values (UP-GNIV) approach is proposed and compared against high utility itemsets with negative item values (HUINIV)-mine. The experimental result shows that the suggested approach performs well.