Charles Bouveyron, Gilles Celeux, T. Brendan Murphy (University College Dublin)

Model-based Clustering and Classification for Data Science

• Gebundenes Buch

Produktbeschreibung

This accessible but rigorous introduction is written for advanced undergraduates and beginning graduate students in data science, as well as researchers and practitioners. It shows how a statistical framework yields sound estimation, testing and prediction methods, using extensive data examples and providing R code for many methods.

Produktdetails

• Cambridge Series in Statistical and Probabilistic Mathematics
• Verlag: Cambridge University Press
• Seitenzahl: 446
• Erscheinungstermin: 29. August 2019
• Englisch
• Abmessung: 260mm x 183mm x 28mm
• Gewicht: 1112g
• ISBN-13: 9781108494205
• ISBN-10: 110849420X
• Artikelnr.: 56022802

Autorenporträt

Charles Bouveyron is Full Professor of Statistics at Université Côte d'Azur and the Chair of Excellence in Data Science at Institut National de Recherche en Informatique et en Automatique (INRIA), Rocquencourt. He has published extensively on model-based clustering, particularly for networks and high-dimensional data.

Inhaltsangabe

1. Introduction
2. Model-based clustering: basic ideas
3. Dealing with difficulties
4. Model-based classification
5. Semi-supervised clustering and classification
6. Discrete data clustering
7. Variable selection
8. High-dimensional data
9. Non-Gaussian model-based clustering
10. Network data
11. Model-based clustering with covariates
12. Other topics
List of R packages
Bibliography
Index.