Statistical theory in clustering

DOI10.1007/BF01908064zbMath0575.62058MaRDI QIDQ1063979

J. A. Hartigan

Publication date: 1985

Published in: Journal of Classification (Search for Journal in Brave)

zbMATH Keywords

clusteringpercolationminimum spanning treeUltrametricempirical distributioncomplete linkageunimodal distributionevolutionary distancesDIP testHierarchical algorithmshigh density regionslikelihood testsMixture methodsSingle linkagetests of unimodality

Mathematics Subject Classification ID

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

Related Items

On a resampling approach for tests on the number of clusters with mixture model-based clustering of tissue samples, Markov-switching model selection using Kullback-Leibler divergence, Parsimonious trees, Self-learning \(K\)-means clustering: a global optimization approach, Comparing classical criteria for selecting intra-class correlated features in Multimix, Robust clustering in regression analysis via the contaminated Gaussian cluster-weighted model, Unnamed Item, Maximum likelihood estimation of Gaussian mixture models without matrix operations, Robust statistics, hypothesis testing, and confidence intervals for persistent homology on metric measure spaces, Resampling approach for cluster model selection, A statistical model of cluster stability, Distances and isomorphism between networks: stability and convergence of network invariants, Aggregated tests based on supremal divergence estimators for non-regular statistical models, An application of the minimal spanning tree approach to the cluster stability problem, Modeling insurance claims via a mixture exponential model combined with peaks-over-threshold approach, Comparison of three hypothesis testing approaches for the selection of the appropriate number of clusters of variables, Issues of robustness and high dimensionality in cluster analysis, Component-trees and multivalued images: structural properties, Finite mixture models and model-based clustering, Performance guarantees for hierarchical clustering, Interactive segmentation based on component-trees, A mesh partitioning algorithm for preserving spatial locality in arbitrary geometries, Model‐based clustering of regression time series data via APECM—an AECM algorithm sung to an even faster beat, Mixtures of multivariate contaminated normal regression models, Clustering in the Presence of Scatter, Metrics on spaces of finite trees, On Application of a ProbabilisticK-Nearest Neighbors Model for Cluster Validation Problem, Computing the volume of a high-dimensional semi-unsupervised hierarchical copula, A randomized algorithm for estimating the number of clusters, Parsimonious mixtures of multivariate contaminated normal distributions, A new non-Archimedean metric on persistent homology, Ultrametric fitting by gradient descent ^*, Testing over-representation of observations in subsets of a DEA technology

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• The dip test of unimodality
• A central limit theorem for k-means clustering
• Asymptotic distributions for clustering criteria
• First-passage percolation on the square lattice
• Hierarchical clustering schemes
• The structure and construction of taxonomic hierarchies
• Consistency of Single Linkage for High-Density Clusters
• A Hybrid Clustering Method for Identifying High-Density Clusters
• A Probability Theory of Cluster Analysis
• Estimating the components of a mixture of normal distributions