A simple approach to sparse clustering

DOI10.1016/j.csda.2016.08.003zbMath1466.62020arXiv1602.07277OpenAlexW2284956838MaRDI QIDQ1658539

Ery Arias-Castro, Xiao Pu

Publication date: 14 August 2018

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

Full work available at URL: https://arxiv.org/abs/1602.07277

zbMATH Keywords

feature selectionhigh-dimensionalsparse clusteringhill-climbing

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Related Items (4)

Clustering sparse binary data with hierarchical Bayesian Bernoulli mixture model ⋮ On efficient model selection for sparse hard and fuzzy center-based clustering algorithms ⋮ Fuzzy Gaussian lasso clustering with application to cancer data ⋮ Feature screening in large scale cluster analysis

Uses Software

• mclust
• sparcl
• COSA
• boost

Cites Work

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• Clustering Objects on Subsets of Attributes (with Discussion)
• A Framework for Feature Selection in Clustering
• Using Evidence of Mixed Populations to Select Variables for Clustering Very High-Dimensional Data
• Variable Selection for Model-Based Clustering

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