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. © 2010 Springer-Verlag Berlin Heidelberg.
Cite
CITATION STYLE
De Araújo, D., Neto, A. D., Melo, J., & Martins, A. (2010). Clustering using elements of information theory. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6354 LNCS, pp. 397–406). https://doi.org/10.1007/978-3-642-15825-4_52
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