A simple method for screening variables before clustering microarray data

DOI10.1016/j.csda.2009.02.001zbMath1453.62127OpenAlexW2071477398MaRDI QIDQ961706

Wojtek Janusz Krzanowski, David J. Hand

Publication date: 1 April 2010

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2009.02.001

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10)

Related Items (4)

Exact methods for variable selection in principal component analysis: guide functions and pre-selection ⋮ A comparison of simulated annealing algorithms for variable selection in principal component analysis and discriminant analysis ⋮ Exact and approximate algorithms for variable selection in linear discriminant analysis ⋮ James-Stein shrinkage to improve \(k\)-means cluster analysis

Uses Software

• SigClust

Cites Work

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• Initializing \(K\)-means batch clustering: A critical evaluation of several techniques
• A variable-selection heuristic for K-means clustering
• Variable selection in clustering via Dirichlet process mixture models
• Higher criticism thresholding: Optimal feature selection when useful features are rare and weak
• Statistical Significance of Clustering for High-Dimension, Low–Sample Size Data
• Identification of Differentially Expressed Genes in High-Density Oligonucleotide Arrays Accounting for the Quantification Limits of the Technology
• Bayesian Variable Selection in Clustering High-Dimensional Data

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