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Problems in data analysis often require the unsupervised partitioning of a dataset into clusters. Many methods exist for such partitioning but most have the weakness of being model-based (most assuming hyper-ellipsoidal clusters) or computationally infeasible in anything more than a three-dimensional data space. We re-consider the notion of cluster analysis in information-theoretic terms and show...
Much work has been published on methods for assessing the probable number of clusters or structures within unknown data sets. This paper aims to look in more detail at two methods, a broad parametric method, based around the assumption of Gaussian clusters and the other a non-parametric method which utilises methods of scale-space filtering to extract robust structures within a data set. It is shown...
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