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Cross-Clustering: A Partial Clustering Algorithm with Automatic Estimation of the Number of Clusters
Four of the most common limitations of the many available clustering methods are: i) the lack of a proper strategy to deal with outliers; ii) the need for a good a priori estimate of the number of clusters to obtain reasonable results; iii) the lack of a method able to detect when partitioning of a...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4807765/ https://www.ncbi.nlm.nih.gov/pubmed/27015427 http://dx.doi.org/10.1371/journal.pone.0152333 |
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author | Tellaroli, Paola Bazzi, Marco Donato, Michele Brazzale, Alessandra R. Drăghici, Sorin |
author_facet | Tellaroli, Paola Bazzi, Marco Donato, Michele Brazzale, Alessandra R. Drăghici, Sorin |
author_sort | Tellaroli, Paola |
collection | PubMed |
description | Four of the most common limitations of the many available clustering methods are: i) the lack of a proper strategy to deal with outliers; ii) the need for a good a priori estimate of the number of clusters to obtain reasonable results; iii) the lack of a method able to detect when partitioning of a specific data set is not appropriate; and iv) the dependence of the result on the initialization. Here we propose Cross-clustering (CC), a partial clustering algorithm that overcomes these four limitations by combining the principles of two well established hierarchical clustering algorithms: Ward’s minimum variance and Complete-linkage. We validated CC by comparing it with a number of existing clustering methods, including Ward’s and Complete-linkage. We show on both simulated and real datasets, that CC performs better than the other methods in terms of: the identification of the correct number of clusters, the identification of outliers, and the determination of real cluster memberships. We used CC to cluster samples in order to identify disease subtypes, and on gene profiles, in order to determine groups of genes with the same behavior. Results obtained on a non-biological dataset show that the method is general enough to be successfully used in such diverse applications. The algorithm has been implemented in the statistical language R and is freely available from the CRAN contributed packages repository. |
format | Online Article Text |
id | pubmed-4807765 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-48077652016-04-05 Cross-Clustering: A Partial Clustering Algorithm with Automatic Estimation of the Number of Clusters Tellaroli, Paola Bazzi, Marco Donato, Michele Brazzale, Alessandra R. Drăghici, Sorin PLoS One Research Article Four of the most common limitations of the many available clustering methods are: i) the lack of a proper strategy to deal with outliers; ii) the need for a good a priori estimate of the number of clusters to obtain reasonable results; iii) the lack of a method able to detect when partitioning of a specific data set is not appropriate; and iv) the dependence of the result on the initialization. Here we propose Cross-clustering (CC), a partial clustering algorithm that overcomes these four limitations by combining the principles of two well established hierarchical clustering algorithms: Ward’s minimum variance and Complete-linkage. We validated CC by comparing it with a number of existing clustering methods, including Ward’s and Complete-linkage. We show on both simulated and real datasets, that CC performs better than the other methods in terms of: the identification of the correct number of clusters, the identification of outliers, and the determination of real cluster memberships. We used CC to cluster samples in order to identify disease subtypes, and on gene profiles, in order to determine groups of genes with the same behavior. Results obtained on a non-biological dataset show that the method is general enough to be successfully used in such diverse applications. The algorithm has been implemented in the statistical language R and is freely available from the CRAN contributed packages repository. Public Library of Science 2016-03-25 /pmc/articles/PMC4807765/ /pubmed/27015427 http://dx.doi.org/10.1371/journal.pone.0152333 Text en © 2016 Tellaroli et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Tellaroli, Paola Bazzi, Marco Donato, Michele Brazzale, Alessandra R. Drăghici, Sorin Cross-Clustering: A Partial Clustering Algorithm with Automatic Estimation of the Number of Clusters |
title | Cross-Clustering: A Partial Clustering Algorithm with Automatic Estimation of the Number of Clusters |
title_full | Cross-Clustering: A Partial Clustering Algorithm with Automatic Estimation of the Number of Clusters |
title_fullStr | Cross-Clustering: A Partial Clustering Algorithm with Automatic Estimation of the Number of Clusters |
title_full_unstemmed | Cross-Clustering: A Partial Clustering Algorithm with Automatic Estimation of the Number of Clusters |
title_short | Cross-Clustering: A Partial Clustering Algorithm with Automatic Estimation of the Number of Clusters |
title_sort | cross-clustering: a partial clustering algorithm with automatic estimation of the number of clusters |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4807765/ https://www.ncbi.nlm.nih.gov/pubmed/27015427 http://dx.doi.org/10.1371/journal.pone.0152333 |
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