Cluster Analysis in Data Mining
Offered By: University of Illinois at Urbana-Champaign via Coursera
Course Description
Overview
Discover the basic concepts of cluster analysis, and then study a set of typical clustering methodologies, algorithms, and applications. This includes partitioning methods such as k-means, hierarchical methods such as BIRCH, and density-based methods such as DBSCAN/OPTICS. Moreover, learn methods for clustering validation and evaluation of clustering quality. Finally, see examples of cluster analysis in applications.
Syllabus
- Course Orientation
- You will become familiar with the course, your classmates, and our learning environment. The orientation will also help you obtain the technical skills required for the course.
- Module 1
- Week 2
- Week 3
- Week 4
- Course Conclusion
- In the course conclusion, feel free to share any thoughts you have on this course experience.
Taught by
Jiawei Han
Tags
Related Courses
Introduction to Artificial IntelligenceStanford University via Udacity Natural Language Processing
Columbia University via Coursera Probabilistic Graphical Models 1: Representation
Stanford University via Coursera Computer Vision: The Fundamentals
University of California, Berkeley via Coursera Learning from Data (Introductory Machine Learning course)
California Institute of Technology via Independent