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Matériel obligatoire et recommandé
Model-Based Clustering and Classification For Data Science: With
Bouveyron, C. / Celeux, G. / Murphy, T.B. / Raftery, A.E.
Éditeur : CAMBRIDGE UNIVERSITY PRESS
ISBN papier: 9781108494205
Parution : 2019
Code produit : 1392734
Catégorisation :
Livres /
Science /
Mathématique /
Mathématiques
Formats disponibles
| Format | Qté. disp. | Prix* | Commander |
|---|---|---|---|
| Livre papier | En rupture de stock** |
Prix membre : 92,15 $ Prix non-membre : 97,00 $ |
*Les prix sont en dollars canadien. Taxes et frais de livraison en sus.
**Ce produits est en rupture de stock mais sera expédié dès qu'ils sera disponible.
Description
Description: 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.