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.
- 5 stars66.41%
- 4 stars23.48%
- 3 stars5.55%
- 2 stars2.02%
- 1 star2.52%
Its Good but explanations can done much better, rest all good in terms of study material, quiz ,and programming assignment.
Very intense and required complex thinking and programming skill
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.
it was a really good experience. this course has given me good exposure to data mining