Evaluation of Clusterings -- Metrics and Visual Support

  title={Evaluation of Clusterings -- Metrics and Visual Support},
  author={Elke Achtert and Sascha Goldhofer and H. Kriegel and Erich Schubert and A. Zimek},
  journal={2012 IEEE 28th International Conference on Data Engineering},
  • Elke Achtert, Sascha Goldhofer, +2 authors A. Zimek
  • Published 2012
  • Computer Science
  • 2012 IEEE 28th International Conference on Data Engineering
  • When comparing clustering results, any evaluation metric breaks down the available information to a single number. However, a lot of evaluation metrics are around, that are not always concordant nor easily interpretable in judging the agreement of a pair of clusterings. Here, we provide a tool to visually support the assessment of clustering results in comparing multiple clusterings. Along the way, the suitability of a couple of clustering comparison measures can be judged in different… CONTINUE READING

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    • 4,819
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    • 124
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    • 578
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    ELKI: A Software System for Evaluation of Subspace Clustering Algorithms
    • 94
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    • 1,057
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    • 13,855
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