Comparing clustering algorithms is much more difficult than comparing classification algorithms, which is due to the unsupervised nature of the task and the lack of a precisely stated objective. We consider explorative cluster analysis as a predictive task (predict regions where data lumps together) and propose a measure to evaluate the performance on an hold-out test set. The performance is discussed for typical situations and results on artificial and real world datasets are presented for partitional, hierarchical, and density-based clustering algorithms. The proposed S-measure successfully senses the individual strengths and weaknesses of each algorithm. © 2009 Springer Berlin Heidelberg.
CITATION STYLE
Höppner, F. (2009). How much true structure has been discovered?Validating explorative clustering on a hold-out test set. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5632 LNAI, pp. 385–397). https://doi.org/10.1007/978-3-642-03070-3_29
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