Recursive Clustering Hematological Data Using Mixture of Exponential Components
Result description
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
Keywords
mixture-based clusteringrecursive mixture estimationexponential components
The result's identifiers
Result code in IS VaVaI
Result on the web
DOI - Digital Object Identifier
Alternative languages
Result language
angličtina
Original language name
Recursive Clustering Hematological Data Using Mixture of Exponential Components
Original language description
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
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10103 - Statistics and probability
Result continuities
Project
GA15-03564S: Clustering and classification using recursive mixture estimation
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of International Conference on Intelligent Informatics and BioMedical Sciences ICIIBMS 2017
ISBN
978-1-5090-6665-0
ISSN
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e-ISSN
—
Number of pages
8
Pages from-to
63-70
Publisher name
IEEE
Place of publication
Piscataway
Event location
Okinawa
Event date
Nov 24, 2017
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000426897300015
Basic information
Result type
D - Article in proceedings
OECD FORD
Statistics and probability
Year of implementation
2017