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 under-stand, 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. Be-sides, we put forward the BIGBAR summarizing method to summarize the rules and present the results intuitively.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:miun-31828 |
| Date | January 2017 |
| Creators | Qing, Yang |
| Publisher | Mittuniversitetet, Avdelningen för informationssystem och -teknologi |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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