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Graph-Theoretic Methods of Cluster Analysis

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Part of the Advances in System Analysis book series (ADSYAN)

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.

Keywords

Minimum Span Tree Data Vector Scale Level Dissimilarity Matrix Creation Time 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Fachmedien Wiesbaden 1988

Authors and Affiliations

  1. 1.NeussGermany

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