Abstract
In this paper we present an application of single cluster visualization (SCV) a technique to visualize single clusters of high-dimensional data. This method maps a single cluster to the plane trying to preserve the relative distances of feature vectors to the corresponding prototype vector. Thus, fuzzy clustering results representing relative distances in the form of a partition matrix as well as hard clustering partitions can be visualized with this technique. The resulting two-dimensional scatter plot illustrates the compactness of a certain cluster and the need of additional prototypes as well. In this work, we will demonstrate the visualization method on a practical application.
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Rehm, F., Klawonn, F., Kruse, R. (2007). Single Cluster Visualization to Optimize Air Traffic Management. In: Decker, R., Lenz, H.J. (eds) Advances in Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70981-7_36
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DOI: https://doi.org/10.1007/978-3-540-70981-7_36
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-70980-0
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