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
Covers great deal of topics and various aspects of clustering
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. Some of the slides have value errors. Explanations for the programming assignments could be better.
This was my favorite course in the whole specialization. Everything is explained very concisely and clearly making the subject matter very easy to understand.
About the Data Mining Specialization
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