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.
CITATION STYLE
Rehm, F., Klawonn, F., & Kruse, R. (2007). Single cluster visualization to optimize air traffic management. In Studies in Classification, Data Analysis, and Knowledge Organization (pp. 319–325). Kluwer Academic Publishers. https://doi.org/10.1007/978-3-540-70981-7_36
Mendeley helps you to discover research relevant for your work.