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Journal of Classification
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29-03-2019

Clustering Large Datasets by Merging K-Means Solutions

Authors: Volodymyr Melnykov, Semhar Michael

Published in: Journal of Classification | Issue 1/2020

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Abstract

Existing clustering methods range from simple but very restrictive to complex but more flexible. The K-means algorithm is one of the most popular clustering procedures due to its computational speed and intuitive construction. Unfortunately, the application of K-means in its traditional form based on Euclidean distances is limited to cases with spherical clusters of approximately the same volume and spread of points. Recent developments in the area of merging mixture components for clustering show good promise. We propose a general framework for hierarchical merging based on pairwise overlap between components which can be readily applied in the context of the K-means algorithm to produce meaningful clusters. Such an approach preserves the main advantage of the K-means algorithm—its speed. The developed ideas are illustrated on examples, studied through simulations, and applied to the problem of digit recognition.
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Metadata
Title
Clustering Large Datasets by Merging K-Means Solutions
Authors
Volodymyr Melnykov
Semhar Michael
Publication date
29-03-2019
Publisher
Springer US
Published in
Journal of Classification / Issue 1/2020
Print ISSN: 0176-4268
Electronic ISSN: 1432-1343
DOI
https://doi.org/10.1007/s00357-019-09314-8

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