Overview

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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.

Syllabus

Foundations of Cluster Analysis

This module introduces the fundamental principles of cluster analysis, a core technique in unsupervised machine learning. Learners will explore the conceptual basis of clustering, understand how clustering groups data points based on similarity, and investigate widely used clustering techniques including hierarchical clustering and k-means. Emphasis is placed on understanding how these methods operate, their practical applications, and the tools used to visualize and evaluate clustering results. By the end of this module, learners will gain a strong conceptual and technical foundation in clustering approaches, preparing them for more advanced machine learning techniques and real-world data segmentation tasks.

Practical Application and Evaluation in SPSS

This module focuses on the implementation and interpretation of cluster analysis techniques using SPSS. Learners will explore practical workflows involving Two-Step clustering and K-means clustering, including the evaluation of clustering quality and methods for handling missing data. Through hands-on demonstrations, students will gain experience with SPSS output interfaces, learn to navigate clustering diagnostics, and apply data preprocessing strategies such as listwise and pairwise deletion. The module equips learners with practical tools to translate unsupervised machine learning concepts into real-world analytical outputs.