Search Algorithms with Randomized Variable Neighborhoods for Solving Series of Clustering Problems

Тип публикации: доклад, тезисы доклада, статья из сборника материалов конференций

Конференция: International Conference on Information Technologies, InfoTech 2020; Varna; Varna

Год издания: 2020

Идентификатор DOI: 10.1109/InfoTech49733.2020.9211070

Ключевые слова: clustering genetic algorithm, greedy agglomerative heuristic, k-means, p-median

Аннотация: In this paper, we propose new algorithms for solving the classical problem of cluster analysis, k-Means, which uses the variable neighborhoods search in randomized neighborhoods formed by running the greedy agglomerative procedures. A comparison with known algorithms including the algorithms with greedy heuristics is given, and the advantage is confirmed experimentally. © 2020 IEEE.

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Издание

Журнал: 2020 34th International Conference on Information Technologies, InfoTech 2020 - Proceedings

Номера страниц: 9211070

Издатель: 34th International Conference on Information Technologies, InfoTech 2020

Персоны

Nasyrov I. (Reshetnev Siberian State University of Science and Technology, prosp. KrasnoyarskiyRabochiy 31, Krasnoyarsk, 660031, Russian Federation)
Gudyma M. (Reshetnev Siberian State University of Science and Technology, prosp. KrasnoyarskiyRabochiy 31, Krasnoyarsk, 660031, Russian Federation)
Kazakovtsev L. (Reshetnev Siberian State University of Science and Technology, prosp. KrasnoyarskiyRabochiy 31, Krasnoyarsk, 660031, Russian Federation, Siberian Federal University, Prosp. Svobodny 79, Krasnoyarsk, 660041, Russian Federation)
Stashkov D. (JSC 'Sinetic' Ulitsa, 3 Internatsionala 127, Novosibirsk, 630009, Russian Federation)

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