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Interactive clustering for exploring multiple data streams at different time scales and granularity
RISE SICS, Stockholm, Sweden.
School of Informatics, University of Skövde, Sweden.
School of Informatics, University of Skövde, Sweden.
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research. Department of Intelligent Systems and Digital Design, Halmstad University, Sweden.ORCID iD: 0000-0002-2859-6155
2019 (English)In: Proceedings of the Workshop on Interactive Data Mining, WIDM 2019, Association for Computing Machinery (ACM), 2019Conference 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. © 2019 Copyright held by the owner/author(s). Publication rights licensed to ACM.

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2019.
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 Computer Systems
Identifiers
URN: urn:nbn:se:hh:diva-41537DOI: 10.1145/3304079.3310286Scopus ID: 2-s2.0-85069762696ISBN: 9781450362962 (print)OAI: oai:DiVA.org:hh-41537DiVA, id: diva2:1391299
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: 2020-02-04 Created: 2020-02-04 Last updated: 2020-02-04Bibliographically approved

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Bouguelia, Mohamed-Rafik

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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