Cluster Analysis in Data Mining

Learn how to take scattered data and organize it into groups, for use in many applications such as market analysis and biomedical data analysis, or taken as a pre-processing step for many data mining tasks.

About The 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, density-based methods such as DBSCAN/OPTICS, probabilistic models and EM algorithm. Learn clustering and methods for clustering high dimensional data, streaming data, graph data, and networked data. Explore concepts and methods for constraint-based clustering and semi-supervised clustering. Finally, see examples of cluster analysis in applications.

Frequently Asked Questions

How does this course fit into the Data Mining Specialization?

This is the third course in the track.



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Recommended Background

For this course you need basic computing proficiency including some programming experience in a typical programming language, such as C++, Java or Python, knowledge of basic concepts of database, artificial intelligence, and statistics.