This book introduces the fundamental approaches to clustering, segmentation, and factor extraction. Chapter 1 covers cluster analysis. After an intuitive example involving the clustering of shells on the beach and the underlying, often unspoken principles of clustering, the hierarchical, partitioning, and TwoStep methods are presented. In hierarchical cluster analysis (CLUSTER), various metrics (e.g., squared Euclidean distance, Pearson correlation, Chi² measure, etc.) and their respective algorithms (Density, Linkage, Ward, etc.) are explained, including their biases (e.g., outliers, chaining). Numerous examples demonstrate how interval data, frequencies, categorical data, and mixed data can be clustered. In partitioning cluster center analysis (k-means, QUICK CLUSTER), you will learn about test statistics for determining cluster centers.