Targeted Mining of Rare High-Utility Patterns
Peifeng Zhang, Jiahui Chen, Shicheng Wan, Wensheng Gan · 2022 IEEE International Conference on Big Data (Big Data) · 2022
Pattern discovery has been widely studied and applied as a classical problem in data mining. As a subfield of itemset mining, identifying high-utility rare itemsets (HURI) can find abnormal but vital patterns in transaction databases. It plays a unique role in real-world scenarios such as anomaly detection and disease detection. However, with large-scale databases, the final results are often massive according to the user-specified threshold. In other words, the mining algorithm ignores the user’s subjective interests and lacks interaction during the mining process. A pattern discovery algorithm may output many useless or uninteresting patterns. To this end, in this paper, we define the problem of mining targeted HURIs and propose a list-based algorithm called TaRP for effectively solving this issue. In addition, based on preliminary research, we propose several effective pruning strategies for improving the algorithm’s performance. TaRP makes the results more interactive and specific by incorporating the user’s prior knowledge during mining. It also has a natural performance advantage with the help of effective strategies. We also evaluated the proposed algorithm on several real-life datasets. The extensive experimental results demonstrate that TaRP not only correctly solves the problem but also has advantages in runtime and memory consumption, especially on dense datasets.