Study on a New Method of Rare Pattern Mining Based on QAR Data
Authors: 
Gang Cheng, Shu Gao, Fan LuoAuthors Info & Claims
Gang Cheng, Shu Gao, Fan LuoAuthors Info & ClaimsPages 155 - 160
Abstract
Most pattern mining methods concentrate on finding frequent pattern. However, rare pattern mining in some cases is more interesting than frequent pattern mining since rare pattern represents unexpected or unknown information. In this paper, we propose a new method of rare pattern mining based on QAR data to mine rare patterns which is related to pilot unsafe manipulation. The proposed method takes advantage of the correlation between QAR parameter and pilot unsafe manipulation to define the item order in item set and makes use of the concept of global support degree. At the same time, 4 types of focused overrun events are used as entry points to explore pilot unsafe manipulation scenarios in the experiments. The experimental results show that the proposed method in this paper reduces execution time. Finally, pilots and regulatory experts are invited to confirm the mining results. The feedback verify method's feasible and effective.
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Index Terms
- Study on a New Method of Rare Pattern Mining Based on QAR Data
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Information
Published In
February 2020
231 pages
ISBN:9781450376785
DOI:10.1145/3384613
Copyright © 2020 ACM.
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In-Cooperation
- The University of Western Australia, Department of Electronic Engineering, University of Western Australia
- Macquarie U., Austarlia
- University of Technology Sydney
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 16 May 2020
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- Research-article
- Research
- Refereed limited
Funding Sources
- Humanities and Social Sciences Project of the Ministry of Education of China
Conference
ICCAE 2020
ICCAE 2020: 2020 12th International Conference on Computer and Automation Engineering
February 14 - 16, 2020
NSW, Sydney, Australia
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