Oct 17th 2016

Cluster Analysis in Data Mining (Coursera)

Taught by:

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.

Cluster Analysis in Data Mining is course 5 of 6 in the Data Mining Specialization.