Entropy 2011, 13(2), 450-465; doi:10.3390/e13020450

Information Theoretic Hierarchical Clustering

Received: 8 December 2010; in revised form: 31 December 2010 / Accepted: 27 January 2011 / Published: 10 February 2011
(This article belongs to the Special Issue Advances in Information Theory)
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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.

AMA Style

Aghagolzadeh M, Soltanian-Zadeh H, Araabi BN. Information Theoretic Hierarchical Clustering. Entropy. 2011; 13(2):450-465.

Chicago/Turabian Style

Aghagolzadeh, Mehdi; Soltanian-Zadeh, Hamid; Araabi, Babak Nadjar. 2011. "Information Theoretic Hierarchical Clustering." Entropy 13, no. 2: 450-465.

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