Arlington, Virginia USA
Apr. 1, 2012 to Apr. 5, 2012
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDE.2012.128
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.
Elke Achtert, Sascha Goldhofer, Hans-Peter Kriegel, Erich Schubert, Arthur Zimek, "Evaluation of Clusterings -- Metrics and Visual Support", ICDE, 2012, 2013 IEEE 29th International Conference on Data Engineering (ICDE), 2013 IEEE 29th International Conference on Data Engineering (ICDE) 2012, pp. 1285-1288, doi:10.1109/ICDE.2012.128