Distance-based clustering challenges for unbiased benchmarking studies

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 sol...

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Bibliographic Details
Published in:Scientific reports Vol. 11; no. 1; pp. 18988 - 12
Main Author: Thrun, Michael C.
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 23.09.2021
Nature Publishing Group
Nature Portfolio
Subjects:
631/114
631/67
639/705
Algorithms
Cluster analysis
Clustering
Comparative analysis
Humanities and Social Sciences
multidisciplinary
Science
Science (multidisciplinary)
ISSN:2045-2322, 2045-2322
Online Access:Get full text
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Summary: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.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-021-98126-1