Efficient mining of high average-utility itemsets with multiple thresholds

Abstract

In this paper, we propose an efficient algorithm to discover HAUIs based on the compact average-utility list structure. A tighter upper-bound model is used to instead of the traditional auub model used in HAUIM to lower the upper-bound value. Three pruning strategies are also respectively developed to facilitate mining performance of HAUIM. Experiments show that the proposed algorithm outperforms the state-of-the-art HAUIM-MMAU algorithm in terms of runtime and memory usage.