Top-k high utility pattern mining with effective threshold raising strategies

In pattern mining, users generally set a minimum threshold to find useful patterns from databases. As a result, patterns with higher values than the user-given threshold are discovered. However, it is hard for the users to determine an appropriate minimum threshold. The reason for this is that they cannot predict the exact number of patterns mined by the threshold and control the mining result precisely, which can lead to performance degradation. To address this issue, top-k mining has been proposed for discovering patterns from ones with the highest value to ones with the kth highest value with setting the desired number of patterns, k. Top-k utility mining has emerged to consider characteristics of real-world databases such as relative importance of items and item quantities with the advantages of top-k mining. Although a relevant algorithm has been suggested in recent years, it generates a huge number of candidate patterns, which results in an enormous amount of execution time. In this paper, we propose an efficient algorithm for mining top-k high utility patterns with highly decreased candidates. For this purpose, we develop three strategies that can reduce the search space by raising a minimum threshold effectively in the construction of a global tree, where they utilize exact and pre-evaluated utilities of itemsets. Moreover, we suggest a strategy to identify actual top-k high utility patterns from candidates with the exact and pre-calculated utilities. Comprehensive experimental results on both real and synthetic datasets show that our algorithm with the strategies outperforms state-of-the-art methods.