Mining High-utility Temporal Paterns on Time Interval based Data

Jun Zhe Wang, Yi Cheng Chen, Wen Yueh Shih, Lin Yang, Yu Shao Liu, Jiun Long Huang*

*Corresponding author for this work

研究成果: Article


In this article, we propose a novel temporal pattern mining problem, named high-utility temporal pattern mining, to fulfill the needs of various applications. Different from classical temporal pattern mining aimed at discovering frequent temporal patterns, high-utility temporal pattern mining is to find each temporal pattern whose utility is greater than or equal to the minimum-utility threshold. To facilitate efficient high-utility temporal pattern mining, several extension and pruning strategies are proposed to reduce the search space. Algorithm HUTPMiner is then proposed to efficiently mine high-utility temporal patterns with the aid of the proposed extension and pruning strategies. Experimental results show that HUTPMiner is able to prune a large number of candidates, thereby achieving high mining efficiency.

期刊ACM Transactions on Intelligent Systems and Technology
出版狀態Published - 七月 2020

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