Learn how to apply and evaluate cluster analysis using SPSS in this hands-on introduction to unsupervised machine learning. This course provides a practical foundation in clustering techniques, helping you understand how to group similar data, interpret clustering results, and make informed decisions in data segmentation tasks.

Designed for learners who want to build analytical skills with SPSS, the course combines core concepts with guided practice. You'll begin by exploring the principles of cluster analysis, comparing hierarchical clustering, K-means clustering, and their applications. You'll also learn how to interpret dendrograms, scree plots, and other visual tools used to assess clustering outcomes. Next, you'll implement clustering workflows in SPSS using K-means and Two-Step Cluster Analysis. You'll apply preprocessing techniques such as listwise and pairwise deletion, evaluate clustering quality with statistical measures including BIC, AIC, and the silhouette coefficient, and interpret SPSS outputs with confidence. By the end of the course, you'll be able to select appropriate clustering techniques, perform cluster analysis in SPSS, evaluate clustering quality, and interpret results for real-world data segmentation tasks. If you're looking for a practical introduction to cluster analysis and unsupervised machine learning with SPSS, this course provides the essential concepts and hands-on experience to get started.