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.
This course is part of the Data Mining Specialization
About this Course
Skills you will gain
- Cluster Analysis
- Data Clustering Algorithms
- K-Means Clustering
- Hierarchical Clustering
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Syllabus - What you will learn from this course
- 5 stars66.41%
- 4 stars23.30%
- 3 stars5.76%
- 2 stars2%
- 1 star2.50%
TOP REVIEWS FROM CLUSTER ANALYSIS IN DATA MINING
Good course for understanding the Cluster Analysis & Algorithms, instructor is very experienced and well explained, thanks
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.
The material is too general, does not provide examples. So it's difficult when doing the exam.
Very informative lectures, wonderful assignments. This course isn't so easy but it gives you real knowledge and useful experience.
About the Data Mining Specialization
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