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Interactive Time Series Clustering with COBRASTS

  • Toon Van CraenendonckEmail author
  • Wannes Meert
  • Sebastijan Dumančić
  • Hendrik Blockeel
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11053)

Abstract

Time series are ubiquitous, resulting in substantial interest in time series data mining. Clustering is one of the most widely used techniques in this setting. Recent work has shown that time series clustering can benefit greatly from small amounts of supervision in the form of pairwise constraints. Such constraints can be obtained by asking the user to answer queries of the following type: should these two instances be in the same cluster? Answering “yes” results in a must-link constraint, “no” results in a cannot-link. In this paper we present an interactive clustering system that exploits such constraints. It is implemented on top of the recently introduced COBRASTS method. The system repeats the following steps until a satisfactory clustering is obtained: it presents several pairwise queries to the user through a visual interface, uses the resulting pairwise constraints to improve the clustering, and shows this new clustering to the user. Our system is readily available and comes with an easy-to-use interface, making it an effective tool for anyone interested in analyzing time series data. Code related to this paper is available at: https://bitbucket.org/toon_vc/cobras_ts/src.

Notes

Acknowledgements

Toon Van Craenendonck is supported by the Agency for Innovation by Science and Technology in Flanders (IWT). This research is supported by Research Fund KU Leuven (GOA/13/010), FWO (G079416N) and FWO-SBO (HYMOP-150033).

References

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    Chen, Y., et al.: The UCR time series classification archive, July 2015. http://www.cs.ucr.edu/eamonn/time_series_data/
  2. 2.
    Van Craenendonck, T., Meert, W., Dumancic, S., Blockeel, H.: COBRAS-TS: a new approach to semi-supervised clustering of time series. https://arxiv.org/abs/1805.00779, under submission, May 2018
  3. 3.
    von Luxburg, U., Williamson, R.C., Guyon, I.: Clustering: science or art? In: Workshop on Unsupervised Learning and Transfer Learning (2014)Google Scholar
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    Wagstaff, K., Cardie, C., Rogers, S., Schroedl, S.: Constrained K-means clustering with background knowledge. In: Proceedings of ICML (2001)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Toon Van Craenendonck
    • 1
    Email author
  • Wannes Meert
    • 1
  • Sebastijan Dumančić
    • 1
  • Hendrik Blockeel
    • 1
  1. 1.Department of Computer ScienceKU LeuvenLeuvenBelgium

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