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Discovery of temporal association rules in multivariate time series
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]

This thesis focuses on mining association rules on multivariate time series. Com-mon association rule mining algorithms can usually only be applied to transactional data, and a typical application is market basket analysis. If we want to mine temporal association rules on time series data, changes need to be made. During temporal association rule mining, the temporal ordering nature of data and the temporal interval between the left and right patterns of a rule need to be considered. This thesis reviews some mining methods for temporal association rule mining, and proposes two similar algorithms for the mining of frequent patterns in single and multivariate time series. Both algorithms are scalable and efficient. In addition, temporal association rules are generated from the patterns found. Finally, the usability and efficiency of the algorithms are demonstrated by evaluating the results.

Place, publisher, year, edition, pages
2017. , p. 59
Keywords [en]
Pattern discovery; temporal association rules; multivariate time series.
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:miun:diva-31576Local ID: DT-V17-A2-009OAI: oai:DiVA.org:miun-31576DiVA, id: diva2:1140711
Subject / course
Computer Engineering DT1
Supervisors
Examiners
Available from: 2017-09-13 Created: 2017-09-13 Last updated: 2017-09-13Bibliographically approved

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CiteExportLink to record
Permanent link

Direct link
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