Simultaneous variable weighting and determining the number of clusters -- a weighted Gaussian means algorithm

DOI10.1016/j.spl.2018.01.015zbMath1414.62233OpenAlexW2788835855MaRDI QIDQ1640944

Saptarshi Chakraborty, Swagatam Das

Publication date: 14 June 2018

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.spl.2018.01.015

zbMATH Keywords

\(k\)-means clusteringnumber of clustersvariable weighting\(G\)-means clustering

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Related Items (1)

Selection of the number of clusters in functional data analysis

Uses Software

• UCI-ml
• JStatCom
• KEEL

Cites Work

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• Variable selection for clustering and classification
• A survey on feature weighting based K-means algorithms
• On the number of groups in clustering
• Clustering with the multivariate normal inverse Gaussian distribution
• Model-based clustering
• Bayesian variable selection for latent class analysis using a collapsed Gibbs sampler
• How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis
• The effectiveness of lloyd-type methods for the k-means problem

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