Visualizing data is a powerful tool for uncovering patterns and insights that might otherwise remain hidden. While there are numerous resources available for data visualization, few focus comprehensively on high-dimensional data visualization. High-dimensional data, or multivariate data, arises when multiple variables are measured for each observation, presenting unique challenges and opportunities for analysis. High-dimensional data visualisation is valuable for understanding dimension reduction methods, unsupervised and supervised classification. This book provides a detailed guide to visualizing high-dimensional data and models using linear projections, with practical examples and R code to help readers explore these fascinating data spaces.
Through this book, readers will learn how to identify patterns, clusters, and anomalies in high-dimensional data that are often obscured in lower-dimensional plots. By integrating visualization techniques with analytical methods, the book aims to enhance the understanding and interpretation of complex data structures, making it an essential resource for anyone working with multivariate data. The book is organised into three parts, following overview and introductory chapters. The dimension reduction chapters cover principal component analysis and nonlinear dimension reduction. The chapters on cluster analysis cover hierarchical and k-means algorithms, model-based and self-organising maps, and finish with ways to communicate results and how to compare different results. The chapters on classification cover linear discriminant analysis, tree and forest algorithms, support vector machines and neural networks.
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