k-POD: A Method for k-Means Clustering of Missing Data
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Publication:5884415
DOI10.1080/00031305.2015.1086685OpenAlexW2191264216WikidataQ57433567 ScholiaQ57433567MaRDI QIDQ5884415
Eric C. Chi, Jocelyn T. Chi, Richard G. Baraniuk
Publication date: 21 March 2023
Published in: The American Statistician (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1411.7013
Related Items (8)
Clustering with missing data: which equivalent for Rubin's rules? ⋮ Multiobjective semisupervised learning with a right‐censored endpoint adapted to the multiple imputation framework ⋮ Clustering with missing and left‐censored data: A simulation study comparing multiple‐imputation‐based procedures ⋮ Imputation strategies for clustering mixed-type data with missing values ⋮ Incomplete clustering analysis via multiple imputation ⋮ Clustering Data with Nonignorable Missingness using Semi-Parametric Mixture Models ⋮ An efficient k‐means‐type algorithm for clustering datasets with incomplete records ⋮ Archetypal Analysis With Missing Data: See All Samples by Looking at a Few Based on Extreme Profiles
Uses Software
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