| bibtype |
C -
Conference Paper (international conference)
|
| ARLID |
0462336 |
| utime |
20240111140923.8 |
| mtime |
20160908235959.9 |
| SCOPUS |
85006049361 |
| WOS |
000391554300037 |
| DOI |
10.1109/IS.2016.7737431 |
| title
(primary) (eng) |
Mixture-based Clustering Non-gaussian Data with Fixed Bounds |
| specification |
| page_count |
7 s. |
| media_type |
C |
|
| serial |
| ARLID |
cav_un_epca*0462335 |
| ISBN |
978-1-5090-1353-1 |
| title
|
Proceedings of 2016 IEEE 8th International Conference on Intelligent Systems |
| page_num |
265-271 |
| publisher |
| place |
Sofia |
| name |
IEEE |
| year |
2016 |
|
|
| keyword |
mixture-based clustering |
| keyword |
recursive mixture estimation |
| keyword |
mixture of uniform distributions |
| keyword |
data-dependent pointer |
| author
(primary) |
| ARLID |
cav_un_auth*0101167 |
| full_dept (cz) |
Zpracování signálů |
| full_dept (eng) |
Department of Signal Processing |
| department (cz) |
ZS |
| department (eng) |
ZS |
| full_dept |
Department of Signal Processing |
| name1 |
Nagy |
| name2 |
Ivan |
| institution |
UTIA-B |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0108105 |
| full_dept (cz) |
Zpracování signálů |
| full_dept |
Department of Signal Processing |
| department (cz) |
ZS |
| department |
ZS |
| full_dept |
Department of Signal Processing |
| name1 |
Suzdaleva |
| name2 |
Evgenia |
| institution |
UTIA-B |
| country |
RU |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0274528 |
| full_dept (cz) |
Zpracování signálů |
| full_dept |
Department of Signal Processing |
| department (cz) |
ZS |
| department |
ZS |
| name1 |
Mlynářová |
| name2 |
Tereza |
| institution |
UTIA-B |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| source |
|
| cas_special |
| project |
| ARLID |
cav_un_auth*0321440 |
| project_id |
GA15-03564S |
| agency |
GA ČR |
|
| abstract
(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. |
| action |
| ARLID |
cav_un_auth*0333078 |
| name |
2016 IEEE 8th International Conference on Intelligent Systems IS'2016 |
| dates |
04.09.2016-06.09.2016 |
| place |
Sofia |
| country |
BG |
|
| RIV |
BB |
| reportyear |
2017 |
| num_of_auth |
3 |
| presentation_type |
PR |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0262262 |
| confidential |
S |
| mrcbC86 |
n.a. Proceedings Paper Computer Science Artificial Intelligence|Computer Science Hardware Architecture |
| arlyear |
2016 |
| mrcbU14 |
85006049361 SCOPUS |
| mrcbU34 |
000391554300037 WOS |
| mrcbU56 |
pdf |
| mrcbU63 |
cav_un_epca*0462335 Proceedings of 2016 IEEE 8th International Conference on Intelligent Systems 978-1-5090-1353-1 265-271 265 271 Sofia IEEE 2016 |
|