On the strengths of the self-updating process clustering algorithm

DOI10.1080/00949655.2015.1049605OpenAlexW2280812514MaRDI QIDQ5222391

Ting-Li Chen, Shang-Ying Shiu

Publication date: 1 April 2020

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1201.1979

zbMATH Keywords

clusteringhierarchical clusteringK-meansmean-shiftgeneralized association plots

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Clustering in the social and behavioral sciences (91C20) Pattern recognition, speech recognition (68T10)

Related Items (1)

Randomized self-updating process for clustering large-scale data

Uses Software

• UCI-ml
• GAP

Cites Work

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• Estimating the Number of Clusters in a Data Set Via the Gap Statistic
• On the weak convergence and central limit theorem of blurring and nonblurring processes with application to robust location estimation
• GAP: a graphical environment for matrix visualization and cluster analysis
• On the convergence and consistency of the blurring mean-shift process
• \(\gamma\)-SUP: a clustering algorithm for cryo-electron microscopy images of asymmetric particles
• The estimation of the gradient of a density function, with applications in pattern recognition
• Model-Based Gaussian and Non-Gaussian Clustering
• Least squares quantization in PCM
• Tight Clustering: A Resampling‐Based Approach for Identifying Stable and Tight Patterns in Data

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