0482566 20240111140952.620171205235959.9 85047411746 000426897300015 10.1109/ICIIBMS.2017.8279700 8 s. P cav_un_epca*0482565978-1-5090-6665-063-70PiscatawayIEEE2017 mixture-based clustering recursive mixture estimation exponential components cav_un_auth*0108105 Suzdaleva Evgenia UTIA-B Zpracování signálů Department of Signal Processing ZS ZS Department of Signal Processing RU Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101167 Nagy Ivan UTIA-B Zpracování signálů Department of Signal Processing ZS ZS Department of Signal Processing Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0349960 Petrouš Matej UTIA-B Zpracování signálů Department of Signal Processing ZS ZS Ústav teorie informace a automatizace AV ČR, v. v. i. pdf http://library.utia.cas.cz/separaty/2017/ZS/suzdaleva-0482566.pdf cav_un_auth*0321440 GA15-03564S GA ČR 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. cav_un_auth*0354635 International Conference on Intelligent Informatics and BioMedical Sciences ICIIBMS 2017 20171124 20171126 Okinawa JP BB 10000 10100 10103 2018 3 PR RVO:67985556 http://hdl.handle.net/11104/0278141 S RIV/67985556:_____/17:00482566!RIV18-AV0-67985556 191975727 Doplnění UT WOS, Scopus a DOI RIV/67985556:_____/17:00482566!RIV18-GA0-67985556 191965055 Doplnění UT WOS, Scopus a DOI 3+4 Proceedings Paper Computer Science Artificial Intelligence|Computer Science Information Systems|Medical Informatics 3+4 Proceedings Paper Computer Science Artificial Intelligence|Computer Science Information Systems|Medical Informatics 3+4 Proceedings Paper Computer Science Artificial Intelligence|Computer Science Information Systems|Medical Informatics 2017 85047411746 SCOPUS PUBMED 000426897300015 WOS pdf 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