Sparse clusterability: testing for cluster structure in high dimensions

Sparse clusterability: testing for cluster structure in high dimensions

Abstract Background Cluster analysis is utilized frequently in scientific theory and applications to separate data into groups. A key assumption in many clustering algorithms is that the data was generated from a population consisting of multiple distinct clusters. Clusterability testing allows user...

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Bibliographic Details
Main Authors: Jose Laborde, Paul A. Stewart, Zhihua Chen, Yian A. Chen, Naomi C. Brownstein
Format: Article
Language:English
Published: BMC 2023-03-01
Series:BMC Bioinformatics
Subjects:
Cluster analysis
Cluster tendency
Clustering
Big data
Dimension reduction
Principal component analysis
Online Access:https://doi.org/10.1186/s12859-023-05210-6

Internet

https://doi.org/10.1186/s12859-023-05210-6

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