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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.
Received: 8 December 2010; in revised form: 31 December 2010 / Accepted: 27 January 2011 / Published: 10 February 2011
Abstract: 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.
Keywords: information theory; Rényi’s entropy; quadratic mutual information; hierarchical clustering; proximity measure
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MDPI and ACS Style
Aghagolzadeh, M.; Soltanian-Zadeh, H.; Araabi, B.N. Information Theoretic Hierarchical Clustering. Entropy 2011, 13, 450-465.
Aghagolzadeh M, Soltanian-Zadeh H, Araabi BN. Information Theoretic Hierarchical Clustering. Entropy. 2011; 13(2):450-465.
Aghagolzadeh, Mehdi; Soltanian-Zadeh, Hamid; Araabi, Babak Nadjar. 2011. "Information Theoretic Hierarchical Clustering." Entropy 13, no. 2: 450-465.