Bouveyron / Celeux / Murphy Model-Based Clustering and Classification for Data Science

With Applications in R
Erscheinungsjahr 2019
ISBN: 978-1-108-64059-6
Verlag: Cambridge University Press
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 0 Seiten

Reihe: Cambridge Series in Statistical and Probabilistic Mathematics

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 observations, and also has open questions about parameter tuning, robustness and uncertainty assessment. This book frames cluster analysis and classification in terms of statistical models, thus yielding principled estimation, testing and prediction methods, and sound answers to the central questions. It builds the basic ideas in an accessible but rigorous way, with extensive data examples and R code; describes modern approaches to high-dimensional data and networks; and explains such recent advances as Bayesian regularization, non-Gaussian model-based clustering, cluster merging, variable selection, semi-supervised and robust classification, clustering of functional data, text and images, and co-clustering. Written for advanced undergraduates in data science, as well as researchers and practitioners, it assumes basic knowledge of multivariate calculus, linear algebra, probability and statistics.

Autoren/Hrsg.

Bouveyron, Charles

Celeux, Gilles

Murphy, T. Brendan

Raftery, Adrian E.

Fachgebiete

• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Epidemiologie, Medizinische Statistik
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Automatische Datenerfassung, Datenanalyse
• Sozialwissenschaften Soziologie | Soziale Arbeit Soziologie Allgemein Empirische Sozialforschung, Statistik
• Mathematik | Informatik Mathematik Stochastik Wahrscheinlichkeitsrechnung
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizinische Mathematik & Informatik
• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
• Wirtschaftswissenschaften Volkswirtschaftslehre Volkswirtschaftslehre Allgemein Wirtschaftsstatistik, Demographie
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining

Inhaltsverzeichnis

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.

Über Autor(innen)

Raftery, Adrian E.
Adrian E. Raftery is the Boeing International Professor of Statistics and Sociology at the University of Washington. He is one of the founding researchers in model-based clustering, having published in the area since 1984.

Murphy, T. Brendan
T. Brendan Murphy is Full Professor in the School of Mathematics and Statistics at University College Dublin. His research interests include model-based clustering, classification, network modeling and latent variable modeling.

Celeux, Gilles
Gilles Celeux is Director of Research Emeritus at Institut National de Recherche en Informatique et en Automatique (INRIA), Rocquencourt. He is one of the founding researchers in model-based clustering, having published extensively in the area for thrity-five years.

Bouveyron, Charles
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.