Тип публикации: статья из журнала
Год издания: 2019
Идентификатор DOI: 10.22190/FUMI1905957R
Ключевые слова: greedy heuristic, clustering problem, gpu, k-Means Problem, variable neighborhoods
Аннотация: The k-means algorithm with the corresponding problem formulation is one of the first methods that researchers use when solving a new automatic grouping (clus-tering) problem. Its improvement, modification and combination with other algorithms are described in the works of many researchers. In this research, we propose new al-gorithmsПоказать полностьюof the Greedy Heuristic Method, which use an idea of the search in variable neighborhoods for solving the classical cluster analysis problem, and allows us to obtain a more accurate and stable result of solving in comparison with the known algorithms. Our computational experiments show that the new algorithms allow us to obtain re-sults with better values of the objective function value (sum of squared distances) in comparison with classical algorithms such as k-means, j-means and genetic algorithms on various practically important datasets. In addition, we present the first results for the GPU realization of the Greedy Heuristic Method.
Журнал: Facta Universitatis, Series: Mathematics and Informatics
Выпуск журнала: Т. 34, № 5
Номера страниц: 957-972
ISSN журнала: 03529665
Место издания: Белград