Abstract
Two ways of defining clusters exist. Clusters can be defined constructively, by statement of a criterion and choice of an algorithm. The term “cluster” usually is left undefined. On the other hand, clusters can be defined as subsets of a sample S, satisfying certain mathematically convenient and evident conditions. In this axiomatic approach to classification theory, the term “cluster” is well-defined. Such pre-defined clusters are usually uncovered using measures of similarity, disparity, dissimilarity or distance, respectively. Rarely the raw data are used directly to find classes by outlining their shapes. In this chapter, we introduce cluster definitions, which are based on graph theory.
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© 1988 Springer Fachmedien Wiesbaden
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Godehardt, E. (1988). Graph-Theoretic Methods of Cluster Analysis. In: Graphs as Structural Models. Advances in System Analysis. Vieweg+Teubner Verlag, Wiesbaden. https://doi.org/10.1007/978-3-322-96310-9_4
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DOI: https://doi.org/10.1007/978-3-322-96310-9_4
Publisher Name: Vieweg+Teubner Verlag, Wiesbaden
Print ISBN: 978-3-528-06312-2
Online ISBN: 978-3-322-96310-9
eBook Packages: Springer Book Archive