Model-based clustering, classification, and density estimation using mclust in R. (2023)
- Record Type:
- Book
- Title:
- Model-based clustering, classification, and density estimation using mclust in R. (2023)
- Main Title:
- Model-based clustering, classification, and density estimation using mclust in R
- Further Information:
- Note: Luca Scrucca, Chris Fraley, T. Brendan Murphy, Adrian E. Raftery.
- Authors:
- Scrucca, Luca
Fraley, Chris
Murphy, T. Brendan, 1972-
Raftery, Adrian E - Contents:
- List of Figures; List of Tables; List of Examples; Preface 1. Introduction; Model-based Clustering and Finite Mixture Modeling mclust Overview Organization of the Book 2. Finite Mixture Models ; Finite Mixture Models Maximum Likelihood Estimation and the EM Algorithm; Issues in Maximum Likelihood Estimation Gaussian Mixture Models Parsimonious Covariance Decomposition EM Algorithm for Gaussian Mixtures Initialization of EM Algorithm Maximum A Posteriori (MAP) Classification Model Selection Information Criteria Likelihood Ratio Testing Resampling-Based Inference 3. Model-based Clustering; Gaussian Mixture Models for Cluster Analysis Clustering in mclust Model Selection BIC ICL Bootstrap Likelihood Ratio Testing Resampling-Based Inference in mclust Clustering Univariate Data Model-Based Agglomerative Hierarchical Clustering Agglomerative Clustering for Large Datasets Initialization in mclust EM Algorithm in mclust Further Considerations 4. Mixture-based Classification; Classification as Supervised Learning Gaussian Mixture Models for Classification Prediction Estimation Classification in mclust Evaluating Classifier Performance Evaluating Predicted Classes: Classification Error Evaluating Class Probabilities: Brier Score Estimating Classifier Performance: Test Set and Resampling-Based Validation Cross-validation in mclust Classification with Unequal Costs of Misclassification Classification with Unbalanced Classes Classification of Univariate Data Semi-supervisedList of Figures; List of Tables; List of Examples; Preface 1. Introduction; Model-based Clustering and Finite Mixture Modeling mclust Overview Organization of the Book 2. Finite Mixture Models ; Finite Mixture Models Maximum Likelihood Estimation and the EM Algorithm; Issues in Maximum Likelihood Estimation Gaussian Mixture Models Parsimonious Covariance Decomposition EM Algorithm for Gaussian Mixtures Initialization of EM Algorithm Maximum A Posteriori (MAP) Classification Model Selection Information Criteria Likelihood Ratio Testing Resampling-Based Inference 3. Model-based Clustering; Gaussian Mixture Models for Cluster Analysis Clustering in mclust Model Selection BIC ICL Bootstrap Likelihood Ratio Testing Resampling-Based Inference in mclust Clustering Univariate Data Model-Based Agglomerative Hierarchical Clustering Agglomerative Clustering for Large Datasets Initialization in mclust EM Algorithm in mclust Further Considerations 4. Mixture-based Classification; Classification as Supervised Learning Gaussian Mixture Models for Classification Prediction Estimation Classification in mclust Evaluating Classifier Performance Evaluating Predicted Classes: Classification Error Evaluating Class Probabilities: Brier Score Estimating Classifier Performance: Test Set and Resampling-Based Validation Cross-validation in mclust Classification with Unequal Costs of Misclassification Classification with Unbalanced Classes Classification of Univariate Data Semi-supervised Classification 5. Model-based Density Estimation; Density Estimation &nbsp … (more)
- Edition:
- 1st
- Publisher Details:
- Boca Raton : Chapman & Hall/CRC
- Publication Date:
- 2023
- Extent:
- 1 online resource (288 pages), illustrations (black and white, and colour)
- Subjects:
- 519.53
Cluster analysis -- Data processing
Gaussian distribution -- Data processing
Estimation theory -- Data processing
R (Computer program language) - Languages:
- English
- ISBNs:
- 9781000868371
9781000868340 - Related ISBNs:
- 9781032234960
9781032234953 - Notes:
- Note: Description based on CIP data; resource not viewed.
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- Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
- Access Usage:
- Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library HMNTS - ELD.DS.763820
- Ingest File:
- 18_057.xml