Distance-based clustering challenges for unbiased benchmarking studies

Abstract Benchmark datasets with predefined cluster structures and high-dimensional biomedical datasets outline the challenges of cluster analysis: clustering algorithms are limited in their clustering ability in the presence of clusters defining distance-based structures resulting in a biased clust...

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Autor principal:	Michael C. Thrun
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/80c3566d0abb42cbb16bd430d6a3c752
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spelling	oai:doaj.org-article:80c3566d0abb42cbb16bd430d6a3c7522021-12-02T18:14:08ZDistance-based clustering challenges for unbiased benchmarking studies10.1038/s41598-021-98126-12045-2322https://doaj.org/article/80c3566d0abb42cbb16bd430d6a3c7522021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-98126-1https://doaj.org/toc/2045-2322Abstract Benchmark datasets with predefined cluster structures and high-dimensional biomedical datasets outline the challenges of cluster analysis: clustering algorithms are limited in their clustering ability in the presence of clusters defining distance-based structures resulting in a biased clustering solution. Data sets might not have cluster structures. Clustering yields arbitrary labels and often depends on the trial, leading to varying results. Moreover, recent research indicated that all partition comparison measures can yield the same results for different clustering solutions. Consequently, algorithm selection and parameter optimization by unsupervised quality measures (QM) are always biased and misleading. Only if the predefined structures happen to meet the particular clustering criterion and QM, can the clusters be recovered. Results are presented based on 41 open-source algorithms which are particularly useful in biomedical scenarios. Furthermore, comparative analysis with mirrored density plots provides a significantly more detailed benchmark than that with the typically used box plots or violin plots.Michael C. ThrunNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Michael C. Thrun Distance-based clustering challenges for unbiased benchmarking studies
description	Abstract Benchmark datasets with predefined cluster structures and high-dimensional biomedical datasets outline the challenges of cluster analysis: clustering algorithms are limited in their clustering ability in the presence of clusters defining distance-based structures resulting in a biased clustering solution. Data sets might not have cluster structures. Clustering yields arbitrary labels and often depends on the trial, leading to varying results. Moreover, recent research indicated that all partition comparison measures can yield the same results for different clustering solutions. Consequently, algorithm selection and parameter optimization by unsupervised quality measures (QM) are always biased and misleading. Only if the predefined structures happen to meet the particular clustering criterion and QM, can the clusters be recovered. Results are presented based on 41 open-source algorithms which are particularly useful in biomedical scenarios. Furthermore, comparative analysis with mirrored density plots provides a significantly more detailed benchmark than that with the typically used box plots or violin plots.
format	article
author	Michael C. Thrun
author_facet	Michael C. Thrun
author_sort	Michael C. Thrun
title	Distance-based clustering challenges for unbiased benchmarking studies
title_short	Distance-based clustering challenges for unbiased benchmarking studies
title_full	Distance-based clustering challenges for unbiased benchmarking studies
title_fullStr	Distance-based clustering challenges for unbiased benchmarking studies
title_full_unstemmed	Distance-based clustering challenges for unbiased benchmarking studies
title_sort	distance-based clustering challenges for unbiased benchmarking studies
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/80c3566d0abb42cbb16bd430d6a3c752
work_keys_str_mv	AT michaelcthrun distancebasedclusteringchallengesforunbiasedbenchmarkingstudies
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Distance-based clustering challenges for unbiased benchmarking studies

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