Efficient list based mining of high average utility patterns with maximum average pruning strategies

High average utility pattern mining is the concept proposed to complement drawbacks of high utility pattern mining by considering lengths of patterns along with the utilities of the patterns. High average utility pattern mining should be able to gratify the anti-monotone property like other pattern mining techniques. Many high average utility pattern mining studies to satisfy the anti-monotone property have been proposed in order to improve various upper-bounds because the performance of pattern mining can be improved efficiently by satisfying the anti-monotone property. Although those upper-bounds can effectively reduce the search space, they still take a lot of cost to calculate all unpromising patterns or cannot find them in advance. Therefore, in this paper, a novel high average utility pattern mining approach is proposed by employing two novel upper-bounds called tight maximum average utility upper-bound and maximum remaining average utility upper-bound. Moreover, a newly suggested list-based structure, TA-List, is designed to adopt two pruning strategies. The proposed technique can efficiently extract high average utility patterns by reducing search space. To evaluate the performance of the proposed method, various experiments using real and synthetic datasets are conducted in terms of runtime, memory usage and scalability and the proposed approach is compared with the state-of-the-art high average utility pattern mining algorithms. The results of experiments show that the suggested algorithm has better performance with regard to runtime, memory usage and scalability. (C) 2020 Elsevier Inc. All rights reserved.