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 stars66.58%
- 4 stars23.29%
- 3 stars5.56%
- 2 stars2.02%
- 1 star2.53%
Very intense and required complex thinking and programming skill
This was my favorite course in the whole specialization. Everything is explained very concisely and clearly making the subject matter very easy to understand.
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
关于 数据挖掘 专项课程
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.