Variable neighborhood search algorithm for k-means clustering

9th International Multidisciplinary Scientific and Research Conference "Modern Issues in Science and Technology" Workshop "Advanced Technologies in Aerospace, Mechanical and Automation Engineering"

自然科学;工业技术

Orlov, V.I.^1 ; Kazakovtsev, L.A.^1,2 ; Rozhnov, I.P.^1 ; Popov, N.A.^1 ; Fedosov, V.V.^1

Reshetnev Siberian State University of Science and Technology, Krasnoyarsky Rabochy av. 31, Krasnoyarsk

660037, Russia^1

Krasnoyarsk State Agrarian University, Mira av. 90, Krasnoyarsk

660049, Russia^2

关键词: Classical problems;    Comparative efficiencies;    Electronic component;    Greedy heuristics;    K;    means clustering;    Multidimensional data;    Objective function values;    Variable neighborhood search;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/450/2/022035/pdf
DOI  :  10.1088/1757-899X/450/2/022035

来源: IOP

【 摘 要 】

We propose new algorithms of Greedy Heuristic Method for solving the classical problem of cluster analysis, k-Means, which allows us to obtain results with better objective function values in comparison with known algorithms such as k-Means and j-Means. Their comparative efficiency is proved by experiment on various data sets including multidimensional data of non-destructive rejection tests of electronic components for the space industry.