Cluster Analysis in Data Mining
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About this course
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.
What you'll learn
- understand basic concepts of cluster analysis
- apply partitioning methods like k-means
- utilize hierarchical clustering methods such as BIRCH
- implement density-based methods including DBSCAN and OPTICS
- evaluate clustering quality and validation
Skills you'll gain
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