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Estimated minimal residual sum of squares as a function of the number of clusters in -means.
It can be observed that expression (18) for is a decreasing function of the number of clusters.
In Figure 3b, we report the BHI as a function of the number of clusters, obtained using different distance matrices.
Inset: Fraction of information as a function of the number of clusters on a linear scale.
The optimal number of clusters to describe the data is determined from the Bayesian Information Criterion BICC) plot as a function of the number of clusters.
This is probably due to the slight downward bias in the second moments; the degree of bias is an inverse function of the number of clusters.
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Clustering strength is controlled via a hyperparameter α, which sets the expected number of clusters as a function of the number of clustered items.
An additional probability mass function (pmf), describing the number of cluster births per cluster lifetime interval, is also extracted from the measurements.
Finally, likelihood based objective functions provide a rational basis for estimating the number of clusters in a data set.
The Matlab functions, linkage and dendrogram, are employed to determine the number of clusters.
In Figure 3b, we show the number of clusters as a function of μ.
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