How Much True Structure Has Been Discovered?

Authors:
F. Höppner
Affiliations:
University of Applied Sciences Braunschweig/Wolfenbüttel, Wolfsburg, Germany D-38440
Venue:
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
Year:
2009

Citing 9
Cited 0

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Pattern Recognition with Fuzzy Objective Function Algorithms
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A Cluster Validity Approach based on Nearest-Neighbor Resampling

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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.