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 based on some similarity criterion. A common practice for dealing with missing values in the context of clustering is to first impute the missing values, and then apply the clustering algorithm on the completed data.

Results

We consider missing values in the context of optimal clustering, which finds an optimal clustering operator with reference to an underlying random labeled point process (RLPP). We show how the missing-value problem fits neatly into the overall framework of optimal clu

SUBMITTER: Boluki S 

PROVIDER: S-EPMC6584727 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature