About The Course
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, density-based methods such as DBSCAN/OPTICS, probabilistic models and EM algorithm. Learn clustering and methods for clustering high dimensional data, streaming data, graph data, and networked data. Explore concepts and methods for constraint-based clustering and semi-supervised clustering. Finally, see examples of cluster analysis in applications.
Frequently Asked Questions
How does this course fit into the Data Mining Specialization?
This is the third course in the track.
Illinois is a world leader in research, teaching and public engagement, distinguished by the breadth of our programs, broad academic excellence, and internationally renowned faculty.
For this course you need basic computing proficiency including some programming experience in a typical programming language, such as C++, Java or Python, knowledge of basic concepts of database, artificial intelligence, and statistics.