Abstract
Evaluation of clustering partitions is a crucial step in data processing. A multitude of measures exists, which – unfortunately – give for one data set various results. In this paper we present a visualization technique to visualize single clusters of high-dimensional data. Our method maps single clusters to the plane trying to preserve membership degrees that describe a data point’s gradual membership to a certain cluster. The resulting scatter plot illustrates separation of the respecting cluster and the need of additional prototypes as well. Since clusters will be visualized individually, additional prototypes can be added locally where they are needed.
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Rehm, F., Klawonn, F., Kruse, R. (2006). Visualization of Single Clusters. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Żurada, J.M. (eds) Artificial Intelligence and Soft Computing – ICAISC 2006. ICAISC 2006. Lecture Notes in Computer Science(), vol 4029. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11785231_69
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DOI: https://doi.org/10.1007/11785231_69
Publisher Name: Springer, Berlin, Heidelberg
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