COURSE DESCRIPTION
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.
Length: 4 Weeks
Effort: 4-6 hours per week
Price: FREE (Add a verified certificate for £36/month)
Provider: University of Illinois at Urbana-Champaign via Coursera
Subject: Computer Science
Level: Intermediate
Languages: English
Instructors: Jiawei Han
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This course is part of the Upper Level Computer Science course program in the Computer Science Degree Path
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Cluster Analysis in Data Mining
By University of Illinois at Urbana-Champaign via Coursera
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