Model-Based Clustering and Classification for Data Science: With Applications in R

✍ Scribed by Bouveyron C

Publisher: Cambridge University Press
Year: 2019
Tongue: English
Leaves: 447
Category: Library

No coin nor oath required. For personal study only.

✦ Table of Contents

Cover......Page 1
Front Matter......Page 3
CAMBRIDGE SERIES IN STATISTICAL AND PROBABILISTIC MATHEMATICS......Page 4
Model-Based Clustering and Classification for Data Science: With Applications in R......Page 5
Copyright......Page 6
Dedication......Page 7
Contents......Page 9
Expanded Contents......Page 12
Preface......Page 17
Acknowledgements......Page 18
1 Introduction......Page 21
2 Model-based Clustering: Basic Ideas......Page 35
3 Dealing with Difficulties......Page 99
4 Model-based Classification......Page 129
5 Semi-supervised Clustering and Classification......Page 154
6 Discrete Data Clustering......Page 183
7 Variable Selection......Page 219
8 High-dimensional Data......Page 237
9 Non-Gaussian Model-based Clustering......Page 279
10 Network Data......Page 312
11 Model-based Clustering with Covariates......Page 351
12 Other Topics......Page 371
List of R Packages......Page 404
Bibliography......Page 406
Author Index......Page 435
Subject Index......Page 443

📜 SIMILAR VOLUMES

Model-Based Clustering and Classificatio

📁 Model-Based Clustering and Classification for Data Science: With Applications in R

✍ Charles Bouveyron; Gilles Celeux; T. Brendan Murphy; Adrian E. Raftery 📂 Library 📅 2019 🏛 Cambridge University Press 🌐 English

Cluster analysis finds groups in data automatically. Most methods have been heuristic and leave open such central questions as: how many clusters are there? Which method should I use? How should I handle outliers? Classification assigns new observations to groups given previously classified observat

Model-Based Clustering and Classificatio

📁 Model-Based Clustering and Classification for Data Science: With Applications in R

✍ Charles Bouveyron, Gilles Celeux, T. Brendan Murphy, Adrian E. Raftery 📂 Library 📅 2019 🏛 Cambridge University Press 🌐 English

Model-Based Clustering, Classification,

📁 Model-Based Clustering, Classification, and Density Estimation Using mclust in R

✍ Luca Scrucca, Chris Fraley, T. Brendan Murphy, and Adrian E. Raftery 📂 Library 📅 2023 🏛 CRC Press 🌐 English

Model-Based Clustering, Classification, and Denisty Estimation Using mclust in R Model-based clustering and classification methods provide a systematic statistical approach to clustering, classification, and density estimation via mixture modeling. The model-based framework allows the problems of

Model-Based Clustering, Classification,

📁 Model-Based Clustering, Classification, and Density Estimation Using mclust in R

✍ Luca Scrucca, Chris Fraley, T. Brendan Murphy, Adrian E. Raftery 📂 Library 📅 2023 🏛 CRC Press 🌐 English

Classification, Clustering, and Data Min

📁 Classification, Clustering, and Data Mining Applications

✍ David Banks, Leanna House, Frederick R. McMorris, Phipps Arabie, Wolfgang Gaul 📂 Library 📅 2004 🏛 Springer 🌐 English

This volume describes new methods with special emphasis on classification and cluster analysis. These methods are applied to problems in information retrieval, phylogeny, medical diagnosis, microarrays, and other active research areas.

Regression Modeling and Data Analysis wi

📁 Regression Modeling and Data Analysis with Applications in R

✍ Samprit Chatterjee, Jeffrey S. Simonoff 📂 Library 📅 2020 🏛 Wiley 🌐 English

Handbook and reference guide for students and practitioners of statistical regression-based analyses in R Handbook of Regression Analysis with Applications in R, Second Edition is a comprehensive and up-to-date guide to