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Pruning and Summarizing the Discovered Time Series Association Rules from Mechanical Sensor Data Qing
School of Computer Science and Technology, Donghua University, China.
School of Computer Science and Technology, Donghua University, China.
Mid Sweden University, Faculty of Science, Technology and Media, Department of Information Systems and Technology. (CSN STC)
2017 (English)In: PROCEEDINGS OF THE 3RD ANNUAL INTERNATIONAL CONFERENCE ON ELECTRONICS, ELECTRICAL ENGINEERING AND INFORMATION SCIENCE (EEEIS 2017), Paris: Atlantis Press, 2017, Vol. 131, p. 40-45Conference paper, Published paper (Refereed)
Abstract [en]

Sensors are widely used in all aspects of our daily life including factories, hospitals and even our homes. Discovering time series association rules from sensor data can reveal the potential relationship between different sensors which can be used in many applications. However, the time series association rule mining algorithms usually produce rules much more than expected. It's hardly to understand, present or make use of the rules. So we need to prune and summarize the huge amount of rules. In this paper, a two-step pruning method is proposed to reduce both the number and redundancy in the large set of time series rules. Besides, we put forward the BIGBAR summarizing method to summarize the rules and present the results intuitively.

Place, publisher, year, edition, pages
Paris: Atlantis Press, 2017. Vol. 131, p. 40-45
Series
Advances in Engineering Research, ISSN 2352-5401
Keywords [en]
Sensor Time Series, Association Rules, Rules Pruning, Rules Summarizing, BIGBAR
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:miun:diva-32364DOI: 10.2991/eeeis-17.2017.7ISI: 000416098500007ISBN: 978-94-6252-400-2 (print)OAI: oai:DiVA.org:miun-32364DiVA, id: diva2:1164257
Conference
3rd Annual International Conference on Electronics, Electrical Engineering and Information Science (EEEIS 2017), Guangzhou, Guangdong, China, September 8-10, 2017
Projects
DAWNAvailable from: 2017-12-11 Created: 2017-12-11 Last updated: 2017-12-21Bibliographically approved

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CiteExportLink to record
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