Summary of the paper
| Title | Fuzzy V-Measure - An Evaluation Method for Cluster Analyses of Ambiguous Data |
|---|---|
| Authors | Jason Utt, Sylvia Springorum, Maximilian Köper and Sabine Schulte Im Walde |
| Abstract | This paper discusses an extension of the V-measure (Rosenberg and Hirschberg, 2007), an entropy-based cluster evaluation metric. While the original work focused on evaluating hard clusterings, we introduce the Fuzzy V-measure which can be used on data that is inherently ambiguous. We perform multiple analyses varying the sizes and ambiguity rates and show that while entropy-based measures in general tend to suffer when ambiguity increases, a measure with desirable properties can be derived from these in a straightforward manner. |
| Topics | Evaluation Methodologies, Other |
Full paper  |
Fuzzy V-Measure - An Evaluation Method for Cluster Analyses of Ambiguous Data |
| Bibtex | @InProceedings{UTT14.829, author = {Jason Utt and Sylvia Springorum and Maximilian Köper and Sabine Schulte Im Walde}, title = {Fuzzy V-Measure - An Evaluation Method for Cluster Analyses of Ambiguous Data}, booktitle = {Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)}, year = {2014}, month = {may}, date = {26-31}, address = {Reykjavik, Iceland}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Hrafn Loftsson and Bente Maegaard and Joseph Mariani and Asuncion Moreno and Jan Odijk and Stelios Piperidis}, publisher = {European Language Resources Association (ELRA)}, isbn = {978-2-9517408-8-4}, language = {english} } |