This foundational course equips learners with the conceptual knowledge and practical skills needed to perform cluster analysis—an essential unsupervised machine learning technique—using SPSS. Through a blend of theoretical exploration and hands-on implementation, learners will define, differentiate, apply, and evaluate key clustering methodologies, including hierarchical methods, k-means clustering, and Two-Step cluster analysis.
In Module 1, learners will examine the fundamental concepts of cluster analysis, understand how different clustering algorithms work, and explore their respective strengths through illustrative examples and comparisons. Emphasis is placed on developing the ability to identify appropriate use cases and interpret clustering structures such as dendrograms and scree plots.
In Module 2, learners will implement clustering techniques in SPSS, including preprocessing strategies such as listwise and pairwise deletion. The module emphasizes analyzing and evaluating clustering outputs, understanding statistical model criteria (e.g., BIC/AIC), and using diagnostic tools like the silhouette coefficient for validating cluster quality.
By the end of this course, learners will be able to apply clustering techniques to real-world datasets, analyze results critically, and make informed decisions in data segmentation tasks using SPSS.