Information theoretic clustering using a k-nearest neighbors approach |
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Authors: | Vidar V Vikjord Robert Jenssen |
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Affiliation: | 1. Microsoft Development Center Norway (MDCN), Tromsø, Norway;2. Electrical Engineering Group, Department of Physics and Technology, University of Tromsø, Norway |
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Abstract: | We develop a new non-parametric information theoretic clustering algorithm based on implicit estimation of cluster densities using the k-nearest neighbors (k-nn) approach. Compared to a kernel-based procedure, our hierarchical k-nn approach is very robust with respect to the parameter choices, with a key ability to detect clusters of vastly different scales. Of particular importance is the use of two different values of k, depending on the evaluation of within-cluster entropy or across-cluster cross-entropy, and the use of an ensemble clustering approach wherein different clustering solutions vote in order to obtain the final clustering. We conduct clustering experiments, and report promising results. |
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Keywords: | Clustering Scale Entropy Divergence k-nn Parzen windowing Information theory |
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