Sep 1, 2010 · We study a general algorithm to improve the accuracy in cluster analysis that employs the James–Stein shrinkage effect in k-means clustering ...
James–Stein shrinkage to improve k-means cluster analysis
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We study a general algorithm to improve the accuracy in cluster analysis that employs the James–Stein shrinkage effect in k-means clustering.
Aug 2, 2022 · We study a general algorithm to improve the accuracy in cluster analysis that employs the James-Stein shrinkage effect in k-means clustering ...
We study a general algorithm to improve the accuracy in cluster analysis that employs the James-Stein shrinkage effect in k-means clustering.
Nov 30, 2009 · We study a general algorithm to improve accuracy in cluster anal- ysis that employs the James-Stein shrinkage effect in k-means cluster-.
Abstract: We study a general algorithm to improve the accuracy in cluster analysis that employs the James-Stein shrinkage effect in k-means clustering. We ...
We propose an improved K-means clustering approach called 'enhanced shrinkage K-means' based on the James-Stein estimator and learning vector quantization (LVQ ...
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In this work, we propose Shrinkage k-means (Sk-means), a novel variant of k-means based on the James-Stein estimator for the mean of a multivariate normal ...
... James–Stein shrinkage to improve k-means cluster analysis. Gao J., Hitchcock D.B.. Q1. Elsevier. Computational Statistics and Data Analysis , 2010 , citations ...
... James-Stein ... (2015), ``Clustering Functional Data,'' an invited book chapter for the Handbook of Cluster Analysis, (ed: Roberto Rocci), CRC Press.