Back to Cluster Analysis in Data Mining
Learner Reviews & Feedback for Cluster Analysis in Data Mining by University of Illinois Urbana-Champaign
408 ratings
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, 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.
Top reviews
DD
Sep 24, 2017
A very good course, it gives me a general idea of how clustering algorithm work.
RG
Jan 24, 2021
The material is too general, does not provide examples. So it's difficult when doing the exam.
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