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Recursive Clustering Hematological Data Using Mixture of Exponential Components
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SYSNO ASEP 0482566 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Recursive Clustering Hematological Data Using Mixture of Exponential Components Author(s) Suzdaleva, Evgenia (UTIA-B) RID, ORCID
Nagy, Ivan (UTIA-B) RID, ORCID
Petrouš, Matej (UTIA-B)Number of authors 3 Source Title Proceedings of International Conference on Intelligent Informatics and BioMedical Sciences ICIIBMS 2017. - Piscataway : IEEE, 2017 - ISBN 978-1-5090-6665-0 Pages s. 63-70 Number of pages 8 s. Publication form Print - P Action International Conference on Intelligent Informatics and BioMedical Sciences ICIIBMS 2017 Event date 24.11.2017 - 26.11.2017 VEvent location Okinawa Country JP - Japan Event type WRD Language eng - English Country JP - Japan Keywords mixture-based clustering ; recursive mixture estimation ; exponential components Subject RIV BB - Applied Statistics, Operational Research OECD category Statistics and probability R&D Projects GA15-03564S GA ČR - Czech Science Foundation (CSF) Institutional support UTIA-B - RVO:67985556 UT WOS 000426897300015 EID SCOPUS 85047411746 DOI https://doi.org/10.1109/ICIIBMS.2017.8279700 Annotation 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.
Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2018
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