Variable neighborhood search algorithm for k-means clustering

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 expe...

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Bibliographic Details
Published in:IOP conference series. Materials Science and Engineering 2018-11, Vol.450 (2), p.22035
Main Authors: Orlov, V I, Kazakovtsev, L A, Rozhnov, I P, Popov, N A, Fedosov, V V
Format: Article
Language:English
Subjects:
Algorithms
Cluster analysis
Clustering
Electronic components
Greedy algorithms
Heuristic methods
Multidimensional data
Nondestructive testing
Search algorithms
Vector quantization
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Summary: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.
ISSN:1757-8981
1757-899X
1757-899X
DOI:10.1088/1757-899X/450/2/022035