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.
Cluster Analysis in Data Mining
University of Illinois at Urbana-Champaign
via Coursera
Instructor(s)
Jiawei Han
University of Illinois at Urbana-Champaign
via Coursera
Free (audit)
English
Paid Certificate Available
Approx. 17 hours to complete
Self paced
Subtitles: Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, Korean, German, Russian, English, Spanish