Studying Complexity of Model-based Clustering

DOI10.1080/03610918.2014.889156zbMath1347.62116OpenAlexW2157451982MaRDI QIDQ2816737

Volodymyr Melnykov, Semhar Michael

Publication date: 25 August 2016

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610918.2014.889156

zbMATH Keywords

overlapmodel-based clusteringadjusted rand indexCARPclustering complexityFowlkes and Mallows index

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Linear regression; mixed models (62J05)

Related Items (2)

An effective strategy for initializing the EM algorithm in finite mixture models ⋮ Clustering large datasets by merging \(K\)-means solutions

Uses Software

• e1071

Cites Work

• Generation of random clusters with specified degree of separation
• OCLUS: an analytic method for generating clusters with known overlap
• Initializing the EM algorithm in Gaussian mixture models with an unknown number of components
• Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models
• Separation index and partial membership for clustering
• A mixture of experts model for rank data with applications in election studies
• Estimating the dimension of a model
• Model-based clustering of high-dimensional data: a review
• Comparing clusterings -- an information based distance
• Finding Groups in Data
• How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis
• Model-Based Clustering, Discriminant Analysis, and Density Estimation
• Variable Selection for Model-Based Clustering

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