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...
Guardado en:
Autor principal: | Michael C. Thrun |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/80c3566d0abb42cbb16bd430d6a3c752 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Publisher Correction: Distance-based clustering challenges for unbiased benchmarking studies
por: Michael C. Thrun
Publicado: (2021) -
Cluster efficiency study through benchmarking
por: Manuela Tvaronavičienė, et al.
Publicado: (2015) -
Pattern of inflammatory immune response determines the clinical course and outcome of COVID-19: unbiased clustering analysis
por: Eunyoung Emily Lee, et al.
Publicado: (2021) -
Entanglement witnesses from mutually unbiased measurements
por: Katarzyna Siudzińska, et al.
Publicado: (2021) -
Head-to-head comparison of clustering methods for heterogeneous data: a simulation-driven benchmark
por: Gregoire Preud’homme, et al.
Publicado: (2021)