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

DOI

10.2991/eeeis-17.2017.7How to use a DOI?

Keywords

Sensor Time Series, Association Rules, Rules Pruning, Rules Summarizing, BIGBAR

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. 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.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Qing YANG
AU  - Shao-Yu WANG
AU  - Ting-Ting ZHANG
PY  - 2017/09
DA  - 2017/09
TI  - Pruning and Summarizing the Discovered Time Series Association Rules from Mechanical Sensor Data
BT  - Proceedings of the 3rd Annual International Conference on Electronics, Electrical Engineering and Information Science (EEEIS 2017)
PB  - Atlantis Press
SP  - 40
EP  - 45
SN  - 2352-5401
UR  - https://doi.org/10.2991/eeeis-17.2017.7
DO  - 10.2991/eeeis-17.2017.7
ID  - YANG2017/09
ER  -