Mining High-utility Temporal Patterns on Time Interval–based Data

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.

Authors

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

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