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Interactive clustering for exploring multiple data streams at different time scales and granularity
RISE SICS, Stockholm, Sweden.
University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment. (Skövde Artificial Intelligence Lab (SAIL))ORCID iD: 0000-0002-2415-7243
University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment. (Skövde Artificial Intelligence Lab (SAIL))ORCID iD: 0000-0003-2973-3112
Department of Intelligent Systems and Digital Design, Halmstad University, Sweden.
2019 (English)In: Proceedings of the Workshop on Interactive Data Mining, WIDM 2019, Association for Computing Machinery (ACM), 2019, p. 1-7, article id 2Conference paper, Published paper (Refereed)
Abstract [en]

We approach the problem of identifying and interpreting clusters over different time scales and granularity in multivariate time series data. We extract statistical features over a sliding window of each time series, and then use a Gaussian mixture model to identify clusters which are then projected back on the data streams. The human analyst can then further analyze this projection and adjust the size of the sliding window and the number of clusters in order to capture the different types of clusters over different time scales. We demonstrate the effectiveness of our approach in two different application scenarios: (1) fleet management and (2) district heating, wherein each scenario, several different types of meaningful clusters can be identified when varying over these dimensions. 

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2019. p. 1-7, article id 2
Keywords [en]
Clustering, Interaction, Time scales, Time series, Fleet operations, Gaussian distribution, Time measurement, Application scenario, Different time scale, Gaussian Mixture Model, Multiple data streams, Multivariate time series, Time-scales, Data mining
National Category
Other Computer and Information Science
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
URN: urn:nbn:se:his:diva-17512DOI: 10.1145/3304079.3310286ISI: 000557255700002Scopus ID: 2-s2.0-85083947162ISBN: 978-1-4503-6296-2 (print)OAI: oai:DiVA.org:his-17512DiVA, id: diva2:1341812
Conference
1st Workshop on Interactive Data Mining, WIDM 2019, co-located with 12th ACM International Conference on Web Search and Data Mining, WSDM 2019, 15 February 2019
Available from: 2019-08-12 Created: 2019-08-12 Last updated: 2021-11-01Bibliographically approved

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Bae, JuheeKarlsson, Alexander

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