Shrinkage Clustering: A Fast and Size-Constrained Algorithm for Biomedical Applications

DOI10.4230/LIPIcs.WABI.2017.11zbMath1443.92100OpenAlexW3021920813MaRDI QIDQ5111805

Hanyang Li, Chenyue W. Hu, Amina A. Qutub

Publication date: 27 May 2020

Full work available at URL: https://doi.org/10.4230/LIPIcs.WABI.2017.11

zbMATH Keywords

clusteringmatrix factorizationgene expressioncancer subtyping

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10) Medical applications (general) (92C50)

Uses Software

• Silhouettes
• UCI-ml
• cluster (R)
• Kernlab
• apcluster
• APCluster

Cites Work

• Unnamed Item
• Unnamed Item
• Estimating the Number of Clusters in a Data Set Via the Gap Statistic
• Clustering by Passing Messages Between Data Points
• Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
• Consensus clustering: A resampling-based method for class discovery and visualization of gene expression microarray data
• Hierarchical clustering schemes
• Introduction to Information Retrieval
• Stability-Based Validation of Clustering Solutions
• Breast Cancer Diagnosis and Prognosis Via Linear Programming

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