Abstract
This paper proposes a new method for core cluster detection prior to unsupervised automatic classification. Based upon a Markov random field model, this approach transforms the set of multidimensional observations into a normalised discret binary set, which represents the observable field. The field of classes is then represented by connex components corresponding to the cores, or prototypes, inside the samples. Classification results of artificially generated data are compared with results obtained by a classical clustering method.
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© 2000 Springer-Verlag Berlin · Heidelberg
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Sbihi, A., Moussa, A., Benmiloud, B., Postaire, JG. (2000). A Markovian Approach to Unsupervised Multidimensional Pattern Classification. In: Kiers, H.A.L., Rasson, JP., Groenen, P.J.F., Schader, M. (eds) Data Analysis, Classification, and Related Methods. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-59789-3_40
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DOI: https://doi.org/10.1007/978-3-642-59789-3_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-67521-1
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