Abstract
This chapter considers the problem of clustering non-Gaussian data with fixed bounds via recursive mixture estimation under the Bayesian methodology. Here a mixture of uniform distributions is taken, where individual clusters are described by mixture components. For the on-line detection of data clusters, the paper proposes a mixture estimation algorithm based on (i) the update of reproducible statistics of uniform components; (ii) the heuristic initialization via the method of moments; (iii) the non-trivial adaptive forgetting technique; (iv) the data-dependent dynamic pointer model. Results of validation experiments are presented.
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The research was supported by project GAČR GA15-03564S.
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Nagy, I., Suzdaleva, E. (2018). Clustering Non-Gaussian Data Using Mixture Estimation with Uniform Components. In: Sgurev, V., Jotsov, V., Kacprzyk, J. (eds) Practical Issues of Intelligent Innovations. Studies in Systems, Decision and Control, vol 140. Springer, Cham. https://doi.org/10.1007/978-3-319-78437-3_14
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DOI: https://doi.org/10.1007/978-3-319-78437-3_14
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