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      • Computing and Mathematical Sciences
      • Computing and Mathematical Sciences Papers
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      Clustering performance on evolving data streams: Assessing algorithms and evaluation measures within MOA

      Kranen, Philipp; Kremer, Hardy; Jensen, Timm; Seidl, Thomas; Bifet, Albert; Homes, Geoff; Pfahringer, Bernhard
      DOI
       10.1109/ICDMW.2010.17
      Link
       www.computer.org
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      Citation
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      Kranen, P., Kremer, H., Jense, T., Seidl, T., Bifet, A.,..., Pfahringer, B. (2010). Clustering performance on evolving data streams: Assessing algorithms and evaluation measures within MOA. In Proceedings of 2010 IEEE International Conference on Data Mining Workshops, Sydney, Australia, December 13 (pp. 1400-1403). Washington, DC, USA: IEEE.
      Permanent Research Commons link: https://hdl.handle.net/10289/5191
      Abstract
      In today's applications, evolving data streams are ubiquitous. Stream clustering algorithms were introduced to gain useful knowledge from these streams in real-time. The quality of the obtained clusterings, i.e. how good they reflect the data, can be assessed by evaluation measures. A multitude of stream clustering algorithms and evaluation measures for clusterings were introduced in the literature, however, until now there is no general tool for a direct comparison of the different algorithms or the evaluation measures. In our demo, we present a novel experimental framework for both tasks. It offers the means for extensive evaluation and visualization and is an extension of the Massive Online Analysis (MOA) software environment released under the GNU GPL License.
      Date
      2010
      Type
      Journal Article
      Publisher
      IEEE Computer Society
      Collections
      • Computing and Mathematical Sciences Papers [1454]
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