Clustering Analysis

The "Clustering Analysis" course introduces students to the fundamental concepts of unsupervised learning, focusing on clustering and dimension reduction techniques. Participants will explore various clustering methods, including partitioning, hierarchical, density-based, and grid-based clustering. Additionally, students will learn about Principal Component Analysis (PCA) for dimension reduction. Through interactive tutorials and practical case studies, students will gain hands-on experience in

• Understand principles and significance of unsupervised learning, clustering, and dimensionality reduction
• Apply clustering techniques to diverse datasets for pattern discovery
• Implement Principal Component Analysis (PCA) for dimensionality reduction and interpret results