Shrinkage Clustering

Identifies optimal number of clusters while partitioning data. Shrinkage Clustering is a non-negative matrix factorization (NMF) based method that optimizes cluster memberships while simultaneously shrinking the number of clusters to an optimum. This algorithm generates clusters of sufficiently large sample sizes as required by the user and can clusters applications with minimum cluster size constraints.

Shrinkage Clustering specifications

Algorithm

Information

Unique identifier	OMICS_26539
Name	Shrinkage Clustering

Maintainer

This tool is not maintained anymore.

Publication for Shrinkage Clustering

Shrinkage Clustering: a fast and size-constrained clustering algorithm for biomedical applications

2018 BMC Bioinformatics

PMCID: 5782397

PMID: 29361928

DOI: 10.1186/s12859-018-2022-8

Shrinkage Clustering institution(s)

Department of Bioengineering, Rice University, Main Street, Houston, TX, USA

Shrinkage Clustering funding source(s)

Supported in part by NSF CAREER 1150645 and NIH R01 GM106027 grants, and a HHMI Med-into-Grad fellowship.

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