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 stars66.33%
- 4 stars23.36%
- 3 stars5.77%
- 2 stars2.01%
- 1 star2.51%
TOP REVIEWS FROM CLUSTER ANALYSIS IN DATA MINING
Covers great deal of topics and various aspects of clustering
it was a really good experience. this course has given me good exposure to data mining
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.
About the Data Mining Specialization
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