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...
Main Author: | Michael C. Thrun |
---|---|
Format: | Article |
Language: | English |
Published: |
Nature Portfolio
2021-09-01
|
Series: | Scientific Reports |
Online Access: | https://doi.org/10.1038/s41598-021-98126-1 |
Similar Items
-
Publisher Correction: Distance-based clustering challenges for unbiased benchmarking studies
by: Michael C. Thrun
Published: (2021-10-01) -
Unbiased Benchmarking in Mobile Networks: The Role of Sampling and Stratification
by: Sonja Tripkovic, et al.
Published: (2023-01-01) -
Exploiting Distance-Based Structures in Data Using an Explainable AI for Stock Picking
by: Michael C. Thrun
Published: (2022-01-01) -
Cluster Analysis of Per Capita Gross Domestic Products
by: Michael C. Thrun
Published: (2018-07-01) -
Unbiased Seamless SAR Image Change Detection Based on Normalized Compression Distance
by: Mihai Coca, et al.
Published: (2019-01-01)