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
Covers great deal of topics and various aspects of clustering
This was my favorite course in the whole specialization. Everything is explained very concisely and clearly making the subject matter very easy to understand.
This is a very good course covering all area of clustering. The only thing I feel a little struggle is some algorithm explained too brief, I prefer some detail step by step examples.
The material is too general, does not provide examples. So it's difficult when doing the exam.
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