– Explains tree-based representations known as dendrograms . It includes both agglomerative (bottom-up) and divisive (top-down) approaches, along with tools for visual comparison and customization using the dendextend package.
: The book is designed so that you can jump into specific chapters without needing to read the entire guide sequentially. Practical Guide to Cluster Analysis in R. Unsup...
The book is organized into five distinct parts, each focusing on a critical phase of the clustering workflow: – Explains tree-based representations known as dendrograms
: Where points can belong to multiple clusters. The book is organized into five distinct parts,
: The author developed the factoextra R package specifically to help users create ggplot2 -based visualizations of multivariate data and clustering results.
– Focuses on methods that divide data into a pre-specified number of groups. Key algorithms include: K-means : The most common partitioning method. K-Medoids (PAM) : More robust to outliers than K-means. CLARA : Designed specifically for clustering large datasets.