High-dimensional data visualisation is valuable for understanding dimension reduction methods, unsupervised and supervised classification. This book is organised into these three topics, following overview and introductory chapters and could form an independent course on visualization.
High-dimensional data visualisation is valuable for understanding dimension reduction methods, unsupervised and supervised classification. This book is organised into these three topics, following overview and introductory chapters and could form an independent course on visualization.
Dianne Cook and Ursula Laa have jointly published numerous papers on methodology for high-dimensional data visualisation in the past decade. This book is a result of these collaborations. Dianne Cook has been researching methods for data visualisation, particularly for exploratory data analysis, and data mining, for more than 30 years. She is a Distinguished Professor of Statistics at Monash University, Fellow of the American Statistical Association, past editor of the Journal of Computational and Graphical Statistics, and the R Journal, Board Member of the R Foundation, and elected member of the International Statistical Institute, and author of numerous R packages. Ursula Laa is an Assistant Professor at the Institute of Statistics of the University of Natural Resources and Life Sciences in Vienna. She works on new methods for the visualisation of multivariate data and models, and on interdisciplinary applications of statistics and data science methods in different fields.
Inhaltsangabe
Preface I Introduction 1 Picturing high dimensions 2 Technical details II Dimension reduction 3 Dimension reduction overview 4 Principal component analysis 5 Non-linear dimension reduction III Cluster analysis 6 Introduction to clustering 7 Spin-and-brush approach 8 Hierarchical clustering 9 k-means clustering 10 Model-based clustering 11 Self-organizing maps 12 Summarising and comparing clustering results IV Supervised classification 13 Introduction to supervised classification 14 Linear discriminant analysis 15 Trees and forests 16 Support vector machines 17 Neural networks and deep learning 18 Diagnostics for classification models References Appendices A Toolbox B Data C Links to Book Code and Additional Data D Glossary Index
Preface I Introduction 1 Picturing high dimensions 2 Technical details II Dimension reduction 3 Dimension reduction overview 4 Principal component analysis 5 Non-linear dimension reduction III Cluster analysis 6 Introduction to clustering 7 Spin-and-brush approach 8 Hierarchical clustering 9 k-means clustering 10 Model-based clustering 11 Self-organizing maps 12 Summarising and comparing clustering results IV Supervised classification 13 Introduction to supervised classification 14 Linear discriminant analysis 15 Trees and forests 16 Support vector machines 17 Neural networks and deep learning 18 Diagnostics for classification models References Appendices A Toolbox B Data C Links to Book Code and Additional Data D Glossary Index
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