Mixture-based Clustering Non-gaussian Data with Fixed Bounds
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F16%3A00462336" target="_blank" >RIV/67985556:_____/16:00462336 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1109/IS.2016.7737431" target="_blank" >http://dx.doi.org/10.1109/IS.2016.7737431</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/IS.2016.7737431" target="_blank" >10.1109/IS.2016.7737431</a>
Alternative languages
Result language
angličtina
Original language name
Mixture-based Clustering Non-gaussian Data with Fixed Bounds
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
BB - Applied statistics, operational research
OECD FORD branch
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Result continuities
Project
<a href="/en/project/GA15-03564S" target="_blank" >GA15-03564S: Clustering and classification using recursive mixture estimation</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of 2016 IEEE 8th International Conference on Intelligent Systems
ISBN
978-1-5090-1353-1
ISSN
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e-ISSN
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Number of pages
7
Pages from-to
265-271
Publisher name
IEEE
Place of publication
Sofia
Event location
Sofia
Event date
Sep 4, 2016
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000391554300037