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 applying clustering and dimension reduction techniques to diverse datasets. By the end of this course, students will be able to: 1. Understand the principles and significance of unsupervised learning, particularly clustering and dimension reduction. 2. Grasp the concepts and applications of partitioning, hierarchical, density-based, and grid-based clustering methods. 3. Explore the mathematical foundations of clustering algorithms to comprehend their workings. 4. Apply clustering techniques to diverse datasets for pattern discovery and data exploration. 5. Comprehend the concept of dimension reduction and its importance in reducing feature space complexity. 6. Implement Principal Component Analysis (PCA) for dimension reduction and interpret the reduced feature space. 7. Evaluate clustering results and dimension reduction effectiveness using appropriate performance metrics. 8. Apply clustering and dimension reduction techniques in real-world case studies to derive meaningful insights. Throughout the course, students will actively engage in tutorials and case studies, strengthening their clustering analysis and dimension reduction skills and gaining practical experience in applying these techniques to diverse datasets. By achieving the learning objectives, participants will be well-equipped to excel in unsupervised learning tasks and make informed decisions using clustering and dimension reduction techniques.

• Introduction and Partitioning Clustering
• This week provides an introduction to unsupervised learning and clustering analysis. You will delve into partitioning clustering methods, such as K-Means and K-Medoids, understanding their principles and applications.
• Hierarchical Clustering
• This week you will explore hierarchical clustering, a method that creates a tree-like structure to represent data similarities.
• Density-based Clustering
• This week focuses on density-based clustering, which groups data points based on their density within the dataset.
• Grid-based Clustering
• Throughout this week, you will explore grid-based clustering, an approach that partitions the data space into grids for efficient clustering.
• Dimension Reduction Methods
• This week introduces dimension reduction techniques as a critical preprocessing step for handling high-dimensional data.
• Case Study
• The final week focuses on a comprehensive case study where you will apply clustering and dimension reduction techniques to solve a real-world problem.