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 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 criteri

SUBMITTER: Thrun MC 

PROVIDER: S-EPMC8460803 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature