Abstract
In this paper a hierarchical agglomerative clustering is introduced. A hierarchy of two unsupervised clustering algorithms is considered. The first algorithm is based on a competitive Neural Network or on a Probabilistic Principal Surfaces approach and the second one on an agglomerative clustering based on both Fisher and Negentropy information. Different definitions of Negentropy information are used and some tests on complex synthetic data are presented.
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© 2006 Springer-Verlag Berlin Heidelberg
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Ciaramella, A., Longo, G., Staiano, A., Tagliaferri, R. (2006). NEC: A Hierarchical Agglomerative Clustering Based on Fisher and Negentropy Information. In: Apolloni, B., Marinaro, M., Nicosia, G., Tagliaferri, R. (eds) Neural Nets. WIRN NAIS 2005 2005. Lecture Notes in Computer Science, vol 3931. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11731177_8
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DOI: https://doi.org/10.1007/11731177_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33183-4
Online ISBN: 978-3-540-33184-1
eBook Packages: Computer ScienceComputer Science (R0)