On the strong consistency of feature-weighted \(k\)-means clustering in a nearmetric space

DOI10.1002/STA4.227MaRDI QIDQ6541502

Swagatam Das, Saptarshi Chakraborty

Publication date: 19 May 2024

Published in: Stat (Search for Journal in Brave)

zbMATH Keywords

consistencyclusteringfeature weightingnearmetric space

Mathematics Subject Classification ID

Statistics (62-XX)

Cites Work

• Title not available (Why is that?)
• Title not available (Why is that?)
• Title not available (Why is that?)
• Title not available (Why is that?)
• Title not available (Why is that?)
• Variable selection for clustering and classification
• A survey on feature weighting based K-means algorithms
• Strong consistency of \(k\)-parameters clustering
• Strong consistency of k-means clustering
• Empirical processes: A survey of results for independent and identically distributed random variables
• Feature-weighted clustering with inner product induced norm based dissimilarity measures: an optimization perspective
• Strong consistency of factorial \(k\)-means clustering
• Bayesian variable selection for latent class analysis using a collapsed Gibbs sampler
• Information theoretic measures for clusterings comparison: variants, properties, normalization and correction for chance
• Clustering for metric and nonmetric distance measures
• Strong Consistency of ReducedK-means Clustering
• The effectiveness of lloyd-type methods for the k-means problem
• Factorial \(k\)-means analysis for two-way data.

Related Items (1)

On the uniform concentration bounds and large sample properties of clustering with Bregman divergences

This page was built for publication: On the strong consistency of feature-weighted \(k\)-means clustering in a nearmetric space

Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6541502)