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
University of Illinois at Urbana-Champaign
The University of Illinois at Urbana-Champaign is a world leader in research, teaching and public engagement, distinguished by the breadth of its programs, broad academic excellence, and internationally renowned faculty and alumni. Illinois serves the world by creating knowledge, preparing students for lives of impact, and finding solutions to critical societal needs.
- 5 stars66.58%
- 4 stars23.29%
- 3 stars5.56%
- 2 stars2.02%
- 1 star2.53%
TOP REVIEWS FROM CLUSTER ANALYSIS IN DATA MINING
Good course for understanding the Cluster Analysis & Algorithms, instructor is very experienced and well explained, thanks
The material is too general, does not provide examples. So it's difficult when doing the exam.
Very intense and required complex thinking and programming skill
Covers great deal of topics and various aspects of clustering
About the Data Mining Specialization
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.
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