Algorithmic paradigms for stability-based cluster validity and model selection statistical methods, with applications to microarray data analysis

DOI10.1016/j.tcs.2012.01.024zbMath1238.68124OpenAlexW1989810298MaRDI QIDQ418747

Filippo Utro, Raffaele Giancarlo

Publication date: 30 May 2012

Published in: Theoretical Computer Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.tcs.2012.01.024

zbMATH Keywords

bioinformaticscomputational biologymachine learningalgorithms and data structuresanalysis of massive datasetsgeneral statistics

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Data structures (68P05) General biology and biomathematics (92B05)

Related Items (1)

DNA combinatorial messages and epigenomics: the case of chromatin organization and nucleosome occupancy in eukaryotic genomes

Uses Software

• SAS
• Mosclust
• bootstrap
• clusfind
• GenClust
• clustervalidation

Cites Work

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• Bagging predictors
• Estimating the Number of Clusters in a Data Set Via the Gap Statistic
• The adjusted rand statistic: A SAS macro
• Problems and results in extremal combinatorics. II
• On some significance tests in cluster analysis
• Database-friendly random projections: Johnson-Lindenstrauss with binary coins.
• Consensus clustering: A resampling-based method for class discovery and visualization of gene expression microarray data
• Bootstrapping cluster analysis: Assessing the reliability of conclusions from microarray experiments
• Resampling Method for Unsupervised Estimation of Cluster Validity
• Approximate nearest neighbors and the fast Johnson-Lindenstrauss transform
• A Resampling Approach to Cluster Validation
• Extensions of Lipschitz mappings into a Hilbert space
• A Method for Comparing Two Hierarchical Clusterings
• Finding Groups in Data
• Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data
• An elementary proof of a theorem of Johnson and Lindenstrauss
• A Sober Look at Clustering Stability
• Learning the parts of objects by non-negative matrix factorization
• The Geometry of Information Retrieval

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