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Information Theory in Scientific Visualization
Open AccessArticle

Information Theoretic Hierarchical Clustering

Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, University of Tehran, PO Box 1439957131, Tehran 14395-515, Iran
Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824, USA
Radiology Image Analysis Laboratory, Henry Ford Health System, Detroit, MI 48202, USA
School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), PO Box 1954856316, Tehran, Iran
Author to whom correspondence should be addressed.
Entropy 2011, 13(2), 450-465;
Received: 8 December 2010 / Revised: 31 December 2010 / Accepted: 27 January 2011 / Published: 10 February 2011
(This article belongs to the Special Issue Advances in Information Theory)
Hierarchical clustering has been extensively used in practice, where clusters can be assigned and analyzed simultaneously, especially when estimating the number of clusters is challenging. However, due to the conventional proximity measures recruited in these algorithms, they are only capable of detecting mass-shape clusters and encounter problems in identifying complex data structures. Here, we introduce two bottom-up hierarchical approaches that exploit an information theoretic proximity measure to explore the nonlinear boundaries between clusters and extract data structures further than the second order statistics. Experimental results on both artificial and real datasets demonstrate the superiority of the proposed algorithm compared to conventional and information theoretic clustering algorithms reported in the literature, especially in detecting the true number of clusters. View Full-Text
Keywords: information theory; Rényi’s entropy; quadratic mutual information; hierarchical clustering; proximity measure information theory; Rényi’s entropy; quadratic mutual information; hierarchical clustering; proximity measure
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Aghagolzadeh, M.; Soltanian-Zadeh, H.; Araabi, B.N. Information Theoretic Hierarchical Clustering. Entropy 2011, 13, 450-465.

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