A Bayesian non-parametric approach for automatic clustering with feature weighting

DOI10.1002/sta4.306MaRDI QIDQ6541606

Debolina Paul, Swagatam Das

Publication date: 19 May 2024

Published in: Stat (Search for Journal in Brave)

zbMATH Keywords

clusteringfeature weightingBayesian non-parametrics

Mathematics Subject Classification ID

Statistics (62-XX)

Cites Work

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• Estimating the Number of Clusters in a Data Set Via the Gap Statistic
• A survey on feature weighting based K-means algorithms
• Selection of the number of clusters via the bootstrap method
• Influential features PCA for high dimensional clustering
• Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
• Feature weighting in \(k\)-means clustering
• Model-based clustering of high-dimensional data: a review
• Simultaneous variable weighting and determining the number of clusters -- a weighted Gaussian means algorithm
• Rates of convergence for Gibbs sampling for variance component models
• Consistent selection of the number of clusters via crossvalidation
• Sampling-Based Approaches to Calculating Marginal Densities
• Finding the Number of Clusters in a Dataset
• Least squares quantization in PCM
• A Framework for Feature Selection in Clustering
• A Novel Fuzzy C-Means Clustering Algorithm
• Convergence of a block coordinate descent method for nondifferentiable minimization
• Bayesian clustering by dynamics

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