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Evaluation of Clusterings -- Metrics and Visual Support

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5 Author(s)
Achtert, E. ; Inst. for Inf., Ludwig-Maximilians-Univ. Munchen, München, Germany ; Goldhofer, S. ; Kriegel, H.-P. ; Schubert, E.
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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 scenarios.

Published in:

Data Engineering (ICDE), 2012 IEEE 28th International Conference on

Date of Conference:

1-5 April 2012