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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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.
This was my favorite course in the whole specialization. Everything is explained very concisely and clearly making the subject matter very easy to understand.
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.
Its Good but explanations can done much better, rest all good in terms of study material, quiz ,and programming assignment.
Good course for understanding the Cluster Analysis & Algorithms, instructor is very experienced and well explained, thanks
Good course. Some of the slides have value errors. Explanations for the programming assignments could be better.
The course is very insightful and very helpful for the data mining studies at university courses.
A very good course, it gives me a general idea of how clustering algorithm work.
Very intense and required complex thinking and programming skill
Very detailed introduction of Clustering techniques.
關於 数据挖掘 專項課程