Nonparametric K-means algorithm with applications in economic and functional data

DOI10.1080/03610926.2020.1752383OpenAlexW3016745791MaRDI QIDQ5079255

Jia-Min Zhang, Zhangmei Feng

Publication date: 25 May 2022

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610926.2020.1752383

zbMATH Keywords

EM algorithmGaussian processesnonparametric regressionmixture modelsK-means algorithm

Mathematics Subject Classification ID

Statistics (62-XX)

Uses Software

• ElemStatLearn
• AS 136
• sparcl

Cites Work

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• Algorithm AS 136: A K-Means Clustering Algorithm
• The strong law of large numbers for k-means and best possible nets of Banach valued random variables
• Strong law of large numbers for measures of central tendency and dispersion of random variables in compact metric spaces
• Strong consistency of k-means clustering
• A central limit theorem for k-means clustering
• Weak limit theorems for univariate \(k\)-mean clustering under a nonregular condition
• Asymptotic distributions for clustering criteria
• Trimmed \(k\)-means: An attempt to robustify quantizers
• Nonparametric Mixture of Regression Models
• On Grouping for Maximum Homogeneity
• Robust Linear Clustering
• Robust Estimation in the Normal Mixture Model Based on Robust Clustering
• How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis
• Robustness Properties of k Means and Trimmed k Means
• A Framework for Feature Selection in Clustering

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