Clustering using elements of information theory

Authors:
Daniel De Araújo;Adrião Dória Neto;Jorge Melo;Allan Martins
Affiliations:
Federal Rural University of Semi-Árido, Angicos, RN, Brasil and Federal University of Rio Grande do Norte, Departament of Computer Engineering and Automation, Natal, RN, Brasil;Federal University of Rio Grande do Norte, Departament of Computer Engineering and Automation, Natal, RN, Brasil;Federal University of Rio Grande do Norte, Departament of Computer Engineering and Automation, Natal, RN, Brasil;Federal University of Rio Grande do Norte, Departament of Computer Engineering and Automation, Natal, RN, Brasil
Venue:
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III
Year:
2010

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Abstract

This paper proposes an algorithm for clustering using an information-theoretic based criterion. The cross entropy between elements in different clusters is used as a measure of quality of the partition. The proposed algorithm uses "classical" clustering algorithms to initialize some small regions (auxiliary clusters) that will be merged to construct the final clusters. The algorithm was tested using several databases with different spatial distributions.