Determining distinct clusters in gene expression data using similarity in principal component subspaces

DOI10.1007/s12572-012-0055-1zbMath1257.62070OpenAlexW2059327440MaRDI QIDQ1933017

Sudhakar Jonnalagadda, Rajagopalan Srinivasan

Publication date: 22 January 2013

Published in: International Journal of Advances in Engineering Sciences and Applied Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s12572-012-0055-1

zbMATH Keywords

clusteringcluster validationPCA

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Measures of association (correlation, canonical correlation, etc.) (62H20) Biochemistry, molecular biology (92C40)

Uses Software

• mclust
• Silhouettes

Cites Work

• Cluster validation techniques for genome expression data
• Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
• MCLUST: Software for model-based cluster analysis
• Between-Groups Comparison of Principal Components
• Well-Separated Clusters and Optimal Fuzzy Partitions
• Bayes Factors
• On clustering validation techniques
• Unnamed Item