Investigation of an optimal number of clusters by the adaptive EM algorithm (CROSBI ID 666722)
Prilog sa skupa u zborniku | sažetak izlaganja sa skupa | međunarodna recenzija
Podaci o odgovornosti
Novoselac, Vedran
engleski
Investigation of an optimal number of clusters by the adaptive EM algorithm
This paper considers the investigation of an optimal number of clusters for datasets that are modeled as the Gaussian mixture. For that purpose, an adaptive method that is based on the modified Expectation Maximization (EM) algorithm is developed. The modification is conducted within the hidden variable of the standard EM algorithm. Assuming that data are multivariate normally distributed where each component of Gaussian mixture corresponds to one cluster, the modification is provided by utilizing the fact that the Mahalanobis distance of samples follows a Chi-square distribution. Besides, the quantity measure is constructed in order to determine number of clusters. The proposed method is presented in several numerical examples.
Clustering ; EM ; Gaussian mixture ; Mahalanobis distance ; Chi-square
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Podaci o prilogu
22-22.
2018.
objavljeno
Podaci o matičnoj publikaciji
Book of Abstracts 17th International Conference on Operational Research KOI 2018
Arnerić, Josip ; Čeh Časni, Anita
Zagreb: Printed by Fratres d.o.o.
1849-5141
Podaci o skupu
17th International Conference on Operational Research (KOI 2018)
predavanje
26.09.2018-28.09.2018
Zadar, Hrvatska