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Pruning and Summarizing the Discovered Time Series Association Rules from Mechanical Sensor Data Qing

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Publication Details

Author list: Zhang, Tingting

Publisher: Atlantis Press

Place: Paris

Publication year: 2017

Start page: 40

End page: 45

Number of pages: 6

ISBN: 978-94-6252-400-2

DOI: http://dx.doi.org/10.2991/eeeis-17.2017.7

View additional information: View in Web of Science


Abstract

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. Its 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.


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