Abstract
Gaining confidence that a clustering algorithm has produced meaningful results and not an accident of its usually heuristic optimization is central to data analysis. This is the issue of validity and we propose here a method by which Support Vector Machines are used to evaluate the separation in the clustering results. However, we not only obtain a method to compare clustering results from different algorithms or different runs of the same algorithm, but we can also filter noise and outliers. Thus, for a fixed data set we can identify what is the most robust and potentially meaningful clustering result. A set of experiments illustrates the steps of our approach.
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Estivill-Castro, V., Yang, J. (2003). Cluster Validity Using Support Vector Machines. In: Kambayashi, Y., Mohania, M., Wöß, W. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2003. Lecture Notes in Computer Science, vol 2737. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45228-7_25
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DOI: https://doi.org/10.1007/978-3-540-45228-7_25
Publisher Name: Springer, Berlin, Heidelberg
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