Abstract
Finite mixture modeling have been applied for different data mining tasks. The majority of the work done concerning finite mixture models has focused on mixtures for continuous data. However, many applications involve and generate discrete data for which discrete mixtures are better suited. In this paper, we investigate the problem of discrete data modeling using finite mixture models. We propose a novel mixture that we call the multinomial generalized Dirichlet mixture. We designed experiments involving spatial color image databases modeling and summarization to show the robustness, flexibility and merits of our approach.
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Bouguila, N., ElGuebaly, W. (2008). On Discrete Data Clustering. In: Washio, T., Suzuki, E., Ting, K.M., Inokuchi, A. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2008. Lecture Notes in Computer Science(), vol 5012. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68125-0_44
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DOI: https://doi.org/10.1007/978-3-540-68125-0_44
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68124-3
Online ISBN: 978-3-540-68125-0
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