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.
About this Course
Skills you will gain
- 5 stars66.41%
- 4 stars23.48%
- 3 stars5.55%
- 2 stars2.02%
- 1 star2.52%
TOP REVIEWS FROM CLUSTER ANALYSIS IN DATA MINING
Good course. Some of the slides have value errors. Explanations for the programming assignments could be better.
Covers great deal of topics and various aspects of clustering
Useful theory. It will be challenging for non-math students. and also lecturer's native language influence iis going to be challening as well to follow along.
This was my favorite course in the whole specialization. Everything is explained very concisely and clearly making the subject matter very easy to understand.
About the Data Mining Specialization
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