Abstract
Assume that n q-dimensional data points have been obtained and subjected to a cluster analysis algorithm. A potential concern is whether the resulting clusters have a “causal” interpretation or whether they are merely consequences of “random” fluctuation. In previous reports, the asymptotic properties of a number of potentially useful combinatorial tests based on the theory of random interval graphs were described. In the present work, comparisons of the asymptotic efficacy of a class of these tests are provided. As a particular illustration of potential applications, we discuss the detection of mixtures of probability distributions and provide some numerical illustrations.
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© 1999 Springer-Verlag Berlin · Heidelberg
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Harris, B., Godehardt, E. (1999). The Comparative Efficacy of Some Combinatorial Tests for Detection of Clusters and Mixtures of Probability Distributions. In: Gaul, W., Locarek-Junge, H. (eds) Classification in the Information Age. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-60187-3_30
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DOI: https://doi.org/10.1007/978-3-642-60187-3_30
Publisher Name: Springer, Berlin, Heidelberg
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