Abstract
In this paper, we propose a statistical model to cluster positive data. The proposed model adopts a mixture of inverted Dirichlet distributions and is learned using expectation-maximization (EM) for parameters estimation and the minimum message length criterion (MML) for model selection. Experimental results using both synthetic and real data are presented to show the advantages of the proposed model.
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© 2011 Springer-Verlag Berlin Heidelberg
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Bdiri, T., Bouguila, N. (2011). Learning Inverted Dirichlet Mixtures for Positive Data Clustering. In: Kuznetsov, S.O., Ślęzak, D., Hepting, D.H., Mirkin, B.G. (eds) Rough Sets, Fuzzy Sets, Data Mining and Granular Computing. RSFDGrC 2011. Lecture Notes in Computer Science(), vol 6743. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21881-1_42
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DOI: https://doi.org/10.1007/978-3-642-21881-1_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21880-4
Online ISBN: 978-3-642-21881-1
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