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
Start working towards your Master's degree
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
The material is too general, does not provide examples. So it's difficult when doing the exam.
A very good course, it gives me a general idea of how clustering algorithm work.
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.
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.
About the Data Mining Specialization
Frequently Asked Questions
When will I have access to the lectures and assignments?
What will I get if I subscribe to this Specialization?
Is financial aid available?
More questions? Visit the Learner Help Center.