Handbook of cluster analysis

Handbook of cluster analysis

Enregistré dans:
Détails bibliographiques
Autres auteurs : Hennig Christian (Éditeur scientifique), Meila Marina (Éditeur scientifique), Murtagh Fionn (Éditeur scientifique), Rocci Roberto (Éditeur scientifique)
Format : Livre
Langue : anglais
Titre complet : Handbook of cluster analysis / edited by Christian Hennig, University College London, UK, Marina Meila, University of Washington, Seattle, USA, Fionn Murtagh, University of Derby, UK, Goldsmiths, University of London, UK, Roberto Rocci, University of Rome Tor Vergata, Italy
Publié : Boca Raton : CRC Press, Taylor & Francis Group , [2016]
Description matérielle : 1 vol. (753 p.)
Collection : Chapman & Hall/CRC handbooks of modern statistical methods (Print)
Contenu : Machine generated contents note: 1.Cluster Analysis: An Overview / Marina Meila. 2.A Brief History of Cluster Analysis / Fionn Murtagh. Section I Optimization Methods. 3.Quadratic Error and k-Means / Boris Mirkin. 4.K-Medoids and Other Criteria for Crisp Clustering / Douglas Steinley. 5.Foundations for Center-Based Clustering: Worst-Case Approximations and Modern Developments / Maria Florina Balcan. Section II Dissimilarity-Based Methods. 6.Hierarchical Clustering / Fionn Murtagh. 7.Spectral Clustering / Marina Meila. Section III Methods Based on Probability Models. 8.Mixture Models for Standard p-Dimensional Euclidean Data / Suren I. Rathnayake. 9.Latent Class Models for Categorical Data / Gerard Govaert. 10.Dirichlet Process Mixtures and Nonparametric Bayesian Approaches to Clustering / Vinayak Rao. 11.Finite Mixtures of Structured Models / Sara Viviani. 12.Time-Series Clustering / Pierpaolo D'Urso. Note continued: 13.Clustering Functional Data / Mark C. Greenwood. 14.Methods Based on Spatial Processes / Volker Schmidt. 15.Significance Testing in Clustering / Christian Hennig. 16.Model-Based Clustering for Network Data / Thomas Brendan Murphy. Section IV Methods Based on Density Modes and Level Sets. 17.A Formulation in Modal Clustering Based on Upper Level Sets / Adelchi Azzalini. 18.Clustering Methods Based on Kernel Density Estimators: Mean-Shift Algorithms / Miguel A. Carreira-Perpinan. 19.Nature-Inspired Clustering / Joshua Knowles. Section V Specific Cluster and Data Formats. 20.Semi-Supervised Clustering / Radha Chitta. 21.Clustering of Symbolic Data / Paula Brito. 22.A Survey of Consensus Clustering / Ayan Acharya. 23.Two-Mode Partitioning and Multipartitioning / Maurizio Vichi. 24.Fuzzy Clustering / Pierpaolo D'Urso. 25.Rough Set Clustering / Gunther Gediga. Section VI Cluster Validation and Further General Issues. Note continued: 26.Method-Independent Indices for Cluster Validation and Estimating the Number of Clusters / Christian Hennig. 27.Criteria for Comparing Clusterings / Marina Meila. 28.Resampling Methods for Exploring Cluster Stability / Friedrich Leisch. 29.Robustness and Outliers / Christian Hennig. 30.Visual Clustering for Data Analysis and Graphical User Interfaces / Josiane Mothe. 31.Clustering Strategy and Method Selection / Christian Hennig
Sujets :
Analyse de regroupements
Mathématiques
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327 1 |a Machine generated contents note: 1.Cluster Analysis: An Overview / Marina Meila  |a 2.A Brief History of Cluster Analysis / Fionn Murtagh  |a Section I Optimization Methods  |a 3.Quadratic Error and k-Means / Boris Mirkin  |a 4.K-Medoids and Other Criteria for Crisp Clustering / Douglas Steinley  |a 5.Foundations for Center-Based Clustering: Worst-Case Approximations and Modern Developments / Maria Florina Balcan  |a Section II Dissimilarity-Based Methods  |a 6.Hierarchical Clustering / Fionn Murtagh  |a 7.Spectral Clustering / Marina Meila  |a Section III Methods Based on Probability Models  |a 8.Mixture Models for Standard p-Dimensional Euclidean Data / Suren I. Rathnayake  |a 9.Latent Class Models for Categorical Data / Gerard Govaert  |a 10.Dirichlet Process Mixtures and Nonparametric Bayesian Approaches to Clustering / Vinayak Rao  |a 11.Finite Mixtures of Structured Models / Sara Viviani  |a 12.Time-Series Clustering / Pierpaolo D'Urso  |a Note continued: 13.Clustering Functional Data / Mark C. Greenwood  |a 14.Methods Based on Spatial Processes / Volker Schmidt  |a 15.Significance Testing in Clustering / Christian Hennig  |a 16.Model-Based Clustering for Network Data / Thomas Brendan Murphy  |a Section IV Methods Based on Density Modes and Level Sets  |a 17.A Formulation in Modal Clustering Based on Upper Level Sets / Adelchi Azzalini  |a 18.Clustering Methods Based on Kernel Density Estimators: Mean-Shift Algorithms / Miguel A. Carreira-Perpinan  |a 19.Nature-Inspired Clustering / Joshua Knowles  |a Section V Specific Cluster and Data Formats  |a 20.Semi-Supervised Clustering / Radha Chitta  |a 21.Clustering of Symbolic Data / Paula Brito  |a 22.A Survey of Consensus Clustering / Ayan Acharya  |a 23.Two-Mode Partitioning and Multipartitioning / Maurizio Vichi  |a 24.Fuzzy Clustering / Pierpaolo D'Urso  |a 25.Rough Set Clustering / Gunther Gediga  |a Section VI Cluster Validation and Further General Issues  |a Note continued: 26.Method-Independent Indices for Cluster Validation and Estimating the Number of Clusters / Christian Hennig  |a 27.Criteria for Comparing Clusterings / Marina Meila  |a 28.Resampling Methods for Exploring Cluster Stability / Friedrich Leisch  |a 29.Robustness and Outliers / Christian Hennig  |a 30.Visual Clustering for Data Analysis and Graphical User Interfaces / Josiane Mothe  |a 31.Clustering Strategy and Method Selection / Christian Hennig 
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