0476595 20240111140943.520170731235959.9 85029307166 10.5220/0006417104490458 10 s. C cav_un_epca*0476594978-989-758-263-9Volume 1449-458SetúbalSCITEPRESS2017 Mixture-based Clustering Recursive Mixture Estimation Uniform Components cav_un_auth*0108105 Suzdaleva Evgenia Zpracování signálů Department of Signal Processing ZS ZS UTIA-B Department of Signal Processing RU Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101167 Nagy Ivan Zpracování signálů Department of Signal Processing ZS ZS UTIA-B Department of Signal Processing Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0205791 Pecherková Pavla Zpracování signálů Department of Signal Processing ZS ZS UTIA-B Department of Signal Processing CZ Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0330517 Likhonina Raissa Zpracování signálů Department of Signal Processing ZS ZS UTIA-B Department of Signal Processing CZ Ústav teorie informace a automatizace AV ČR, v. v. i. pdf http://library.utia.cas.cz/separaty/2017/ZS/suzdaleva-0476595.pdf cav_un_auth*0321440 GA15-03564S GA ČR The paper deals with a task of initialization of the recursive mixture estimation for the case of uniform components. This task is significant as a part of mixture-based clustering, where data clusters are described by the uniform distributions. The issue is extensively explored for normal components. However, sometimes the assumption of normality is not suitable or limits potential application areas (e.g., in the case of data with fixed bounds). The use of uniform components can be beneficial for these cases. Initialization is always a critical task of the mixture estimation. Within the considered recursive estimation algorithm the key point of its initialization is a choice of initial statistics of components. The paper explores several initialization approaches and compares results of clustering with a theoretical counterpart. Experiments with real data are demonstrated. cav_un_auth*0348184 The 14th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2017) 20170726 20170728 Madrid ES BB 10000 10100 10103 2018 4 4 A hod 4ah 20231122142550.2 PR RVO:67985556 http://hdl.handle.net/11104/0273534 1 Department of Signal Processing UTIA-B 10103 STATISTICS & PROBABILITY S 2017 Soubory v repozitáři: suzdaleva-0476595.pdf 85029307166 SCOPUS PUBMED WOS pdf cav_un_epca*0476594 Proceedings of the 14th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2017) Volume 1 978-989-758-263-9 449 458 Setúbal SCITEPRESS 2017