A new algorithm for clustering based on kernel density estimation

DOI10.1080/02664763.2016.1277191OpenAlexW2573916904MaRDI QIDQ5138996

M. Kleina, Luiz Carlos Matioli, E. A. Leite, Sandrina Rafaela Andrade Santos

Publication date: 4 December 2020

Published in: Journal of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/02664763.2016.1277191

zbMATH Keywords

kernel density estimationGaussian kerneloptimization methodclustering dataClusterKDE algorithm

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (4)

A new measure for assessment of clustering based on kernel density estimation ⋮ A new nonparametric interpoint distance-based measure for assessment of clustering ⋮ ClusterKDE ⋮ MulticlusterKDE: a new algorithm for clustering based on multivariate kernel density estimation

Uses Software

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• Silhouettes
• KernSmooth
• AS 136
• DENCLUE

Cites Work

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• Algorithm AS 136: A K-Means Clustering Algorithm
• Data mining. Concepts and techniques
• An advancement in clustering via nonparametric density estimation
• A variable bandwidth selector in multivariate kernel density estimation
• Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
• A comparative study of several smoothing methods in density estimation
• Density estimation
• A Brief Survey of Bandwidth Selection for Density Estimation
• On optimal data-based bandwidth selection in kernel density estimation
• Numerical Optimization
• Miscellanea. Data sharpening as a prelude to density estimation
• On Estimation of a Probability Density Function and Mode

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