Approximate Confidence Intervals for the Number of Clusters

DOI10.2307/2289862zbMath0676.62052OpenAlexW4236126648MaRDI QIDQ3831883

Lloyd Fisher, John W. Van Ness, Roger Peck

Publication date: 1989

Full work available at URL: https://doi.org/10.2307/2289862

zbMATH Keywords

data reductionloss functionsimulation studyparsimonystrong consistencyloss of informationoptimal clusteringapproximate confidence boundsboostrap-based procedurek-means clusterings

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Related Items

Classification of binary vectors by stochastic complexity., A statistical view of clustering performance through the theory of \(U\)-processes, An automated robust algorithm for clustering multivariate data, MCS: A method for finding the number of clusters, Space-conserving agglomerative algorithms

Uses Software

• AS 136