Тип публикации: статья из журнала
Год издания: 2025
Идентификатор DOI: 10.22190/fumi240616041m
Аннотация: <jats:p>We proposed algorithms for objects clustering based on self-organizing Kohonen maps using various methods of extracting factors (factor analysis: Principal Component Analysis, Maximum Likelihood Estimation, Principal Component Analysis based on Singular Value Decomposition). We performed experiments with different distance Показать полностьюmeasures (Mahalanobis, Euclidean, squared Euclidean, Manhattan), various ways of neuron weights initialization (random, with choice of weight coefficients from a dataset). The computational experiments showed that the use methods of extracting factors in the SOM algorithm improves the accuracy of clustering in most cases. Moreover, clustering accuracy decreases with increasing number of homogeneous batches in a mixed lot.</jats:p>
Журнал: Facta Universitatis, Series: Mathematics and Informatics
Выпуск журнала: Т. 40, № 3
Номера страниц: 563
ISSN журнала: 03529665
Место издания: Белград