Optimal clustering with missing values

Optimal clustering with missing values

Abstract Background Missing values frequently arise in modern biomedical studies due to various reasons, including missing tests or complex profiling technologies for different omics measurements. Missing values can complicate the application of clustering algorithms, whose goals are to group points...

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Bibliographic Details
Main Authors: Shahin Boluki, Siamak Zamani Dadaneh, Xiaoning Qian, Edward R. Dougherty
Format: Article
Language:English
Published: BMC 2019-06-01
Series:BMC Bioinformatics
Subjects:
Clustering
Missing data
Optimal design
Pattern recognition
Online Access:http://link.springer.com/article/10.1186/s12859-019-2832-3

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http://link.springer.com/article/10.1186/s12859-019-2832-3

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