arrow
Return

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

delete2020-05-25
delete2
PRE
AI
J
Jun-Zhe Wang
Y
Yi-Cheng Chen
W
Wen-Yueh Shih
L
Lin Yang
J
Jiun‐Long Huang *
DOI:10.1145/3391230delete
deleteOriginal
deleteOriginal request for help
deleteShare
deleteSave
Abstract

Abstract

En 中文
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.
Keywords:
High-utility temporal pattern
temporal pattern
high utility
interval-based data
data mining
AI Summary

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.

Journal

ACM Transactions on Intelligent Systems and Technology cover
ACM Transactions on Intelligent Systems and Technology
IF:
6.6
Papers:
1.5K
Citations:
6.2K

Organization

N
National Yang Ming Chiao Tung University
Scholars:
2.5W
Papers: 2.3W
Citations: 2.2W
N
National Central University
Scholars:
1.0W
Papers: 8.5K
Citations: 6.4K