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 stars23.40%
- 3 stars5.59%
- 2 stars2.03%
- 1 star2.54%
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.
Good course for understanding the Cluster Analysis & Algorithms, instructor is very experienced and well explained, thanks
A very good course, it gives me a general idea of how clustering algorithm work.
關於 数据挖掘 專項課程
The Data Mining Specialization teaches data mining techniques for both structured data which conform to a clearly defined schema, and unstructured data which exist in the form of natural language text. Specific course topics include pattern discovery, clustering, text retrieval, text mining and analytics, and data visualization. The Capstone project task is to solve real-world data mining challenges using a restaurant review data set from Yelp.