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Pruning and summarizing discovered time series association rules
Mid Sweden University, Faculty of Science, Technology and Media, Department of Information Systems and Technology.
2017 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
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 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.

Place, publisher, year, edition, pages
2017. , p. 58
Keyword [en]
time series association rules, rule pruning, rule summary
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:miun:diva-31828Local ID: DT-V17-A2-007OAI: oai:DiVA.org:miun-31828DiVA, id: diva2:1148689
Subject / course
Computer Engineering DT1
Supervisors
Examiners
Available from: 2017-10-12 Created: 2017-10-12 Last updated: 2017-10-12Bibliographically approved

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fulltext(1702 kB)64 downloads
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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf