Abstract
An original on-line mixture model-based clustering algorithm is presented in this paper. The proposed algorithm is a stochastic gradient ascent derived from the Classification EM (CEM) algorithm. It generalizes the on-line k-means algorithm. Using synthetic data sets, the proposed algorithm is compared to CEM and another on-line clustering algorithm. The results show that the proposed method provides a fast and accurate estimation when mixture components are relatively well separated.
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© 2005 Springer-Verlag Berlin Heidelberg
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Samé, A., Govaert, G., Ambroise, C. (2005). A Mixture Model-Based On-line CEM Algorithm. In: Famili, A.F., Kok, J.N., Peña, J.M., Siebes, A., Feelders, A. (eds) Advances in Intelligent Data Analysis VI. IDA 2005. Lecture Notes in Computer Science, vol 3646. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11552253_34
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DOI: https://doi.org/10.1007/11552253_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28795-7
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