Abstract
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.
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Höppner, F. (2009). How Much True Structure Has Been Discovered?. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2009. Lecture Notes in Computer Science(), vol 5632. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03070-3_29
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DOI: https://doi.org/10.1007/978-3-642-03070-3_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03069-7
Online ISBN: 978-3-642-03070-3
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