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.
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- 5 stars66.41%
- 4 stars23.48%
- 3 stars5.55%
- 2 stars2.02%
- 1 star2.52%
CLUSTER ANALYSIS IN DATA MINING의 최상위 리뷰
Its Good but explanations can done much better, rest all good in terms of study material, quiz ,and programming assignment.
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.
Very intense and required complex thinking and programming skill
This was my favorite course in the whole specialization. Everything is explained very concisely and clearly making the subject matter very easy to understand.
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