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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.
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.
In the course conclusion, feel free to share any thoughts you have on this course experience.