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
-
To support our site, MoocLab may be compensated by some course providers through affiliate links. -
Dismiss Notice
This course is part of the Upper Level Computer Science course program in the Computer Science Degree Path
Click here to see all Upper Level Computer Science courses

Cluster Analysis in Data Mining
By University of Illinois at Urbana-Champaign via Coursera
Tags: