CPS analysis: self-contained validation of biomedical data clustering | Zendy

Lixiang Zhang | Zendy; Lin Lin | Zendy; Jia Li | Zendy

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CPS analysis: self-contained validation of biomedical data clustering

Author(s) -

Lixiang Zhang,

Lin Lin,

Jia Li

Publication year - 2020

Publication title -

bioinformatics

Language(s) - Uncategorized

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btaa165

Subject(s) - computer science , cluster analysis , data mining , set (abstract data type) , visualization , data set , cluster (spacecraft) , class (philosophy) , dimension (graph theory) , r package , data science , machine learning , artificial intelligence , mathematics , computational science , pure mathematics , programming language

Cluster analysis is widely used to identify interesting subgroups in biomedical data. Since true class labels are unknown in the unsupervised setting, it is challenging to validate any cluster obtained computationally, an important problem barely addressed by the research community.

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