| bibtype |
C -
Conference Paper (international conference)
|
| ARLID |
0482566 |
| utime |
20240111140952.6 |
| mtime |
20171205235959.9 |
| SCOPUS |
85047411746 |
| WOS |
000426897300015 |
| DOI |
10.1109/ICIIBMS.2017.8279700 |
| title
(primary) (eng) |
Recursive Clustering Hematological Data Using Mixture of Exponential Components |
| specification |
| page_count |
8 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0482565 |
| ISBN |
978-1-5090-6665-0 |
| title
|
Proceedings of International Conference on Intelligent Informatics and BioMedical Sciences ICIIBMS 2017 |
| page_num |
63-70 |
| publisher |
| place |
Piscataway |
| name |
IEEE |
| year |
2017 |
|
|
| keyword |
mixture-based clustering |
| keyword |
recursive mixture estimation |
| keyword |
exponential components |
| author
(primary) |
| ARLID |
cav_un_auth*0108105 |
| name1 |
Suzdaleva |
| name2 |
Evgenia |
| institution |
UTIA-B |
| full_dept (cz) |
Zpracování signálů |
| full_dept (eng) |
Department of Signal Processing |
| department (cz) |
ZS |
| department (eng) |
ZS |
| full_dept |
Department of Signal Processing |
| country |
RU |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0101167 |
| name1 |
Nagy |
| name2 |
Ivan |
| institution |
UTIA-B |
| full_dept (cz) |
Zpracování signálů |
| full_dept |
Department of Signal Processing |
| department (cz) |
ZS |
| department |
ZS |
| full_dept |
Department of Signal Processing |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0349960 |
| name1 |
Petrouš |
| name2 |
Matej |
| institution |
UTIA-B |
| full_dept (cz) |
Zpracování signálů |
| full_dept |
Department of Signal Processing |
| department (cz) |
ZS |
| department |
ZS |
| 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) |
The paper deals with the mixture-based clustering of anonymized data of patients with leukemia. The presented clustering algorithm is based on the recursive Bayesian mixture estimation for the case of exponential components and the data-dependent dynamic pointer model. The main contribution of the paper is the online performance of clustering, which allows us to actualize the statistics of components and the pointer model with each new measurement. Results of the application of the algorithm to the clustering of hematological data are demonstrated and compared with theoretical counterparts.\n |
| action |
| ARLID |
cav_un_auth*0354635 |
| name |
International Conference on Intelligent Informatics and BioMedical Sciences ICIIBMS 2017 |
| dates |
20171124 |
| mrcbC20-s |
20171126 |
| place |
Okinawa |
| country |
JP |
|
| RIV |
BB |
| FORD0 |
10000 |
| FORD1 |
10100 |
| FORD2 |
10103 |
| reportyear |
2018 |
| num_of_auth |
3 |
| presentation_type |
PR |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0278141 |
| confidential |
S |
| mrcbC83 |
RIV/67985556:_____/17:00482566!RIV18-AV0-67985556 191975727 Doplnění UT WOS, Scopus a DOI |
| mrcbC83 |
RIV/67985556:_____/17:00482566!RIV18-GA0-67985556 191965055 Doplnění UT WOS, Scopus a DOI |
| mrcbC86 |
3+4 Proceedings Paper Computer Science Artificial Intelligence|Computer Science Information Systems|Medical Informatics |
| mrcbC86 |
3+4 Proceedings Paper Computer Science Artificial Intelligence|Computer Science Information Systems|Medical Informatics |
| mrcbC86 |
3+4 Proceedings Paper Computer Science Artificial Intelligence|Computer Science Information Systems|Medical Informatics |
| arlyear |
2017 |
| mrcbU14 |
85047411746 SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
000426897300015 WOS |
| mrcbU56 |
pdf |
| mrcbU63 |
cav_un_epca*0482565 Proceedings of International Conference on Intelligent Informatics and BioMedical Sciences ICIIBMS 2017 978-1-5090-6665-0 63 70 Piscataway IEEE 2017 |
|