# Bibliography

Conference Paper (international conference)

### Mixture-based Clustering Non-gaussian Data with Fixed Bounds

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**: **Proceedings of 2016 IEEE 8th International Conference on Intelligent Systems, p. 265-271

**: **2016 IEEE 8th International Conference on Intelligent Systems IS'2016,
(Sofia, BG, 04.09.2016-06.09.2016)

**: **GA15-03564S, GA ČR

**: **mixture-based clustering,
recursive mixture estimation,
mixture of uniform distributions,
data-dependent pointer

**: **http://library.utia.cas.cz/separaty/2016/ZS/suzdaleva-0462336.pdf

**(eng): **This paper deals with clustering non-gaussian data with fixed bounds. It considers the problem using recursive mixture estimation algorithms under the Bayesian methodology. Such a solution is often desired in areas, where the assumption of normality of modeled data is rather questionable and brings a series of limitations (e.g., non-negative, bounded data, etc.). Here for modeling the data a mixture of uniform distributions is taken, where individual clusters are described by mixture components. For the on-line detection of clusters of measured bounded data, 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. The approach is validated using realistic traffic flow simulations.

**: **BB