Course Review – Cluster Analysis in Data Mining (Jiawei Han, UIUC)

Here is my review of Apr-Jun 2015 Coursera offering of Cluster Analysis in Data Mining, which is the 3rd course in Data Mining Specialization. The course is ranked 2.7 out of 5 (poor), while I passed with 100% grade.

Technologies and Material. Cluster analysis is an essential unsupervised learning technique widely employed in deriving new knowledge. The course describes mathematically and outlines the examples for most modern cluster analysis methods. Discussed are partitioning algorithms (K-means and its derivatives), hierarchical methods (BIRCH), density + grid-based (DBSCAN), probabilistic models (Gaussian mixture), graph algorithms (KNN), and many more. Clustering of various kinds of data is outlined. Expectation-Maximization (EM) algorithm is first mentioned in this course. One optional programming assignment is provided as an experiment.

Instructor/lectures. Jiawei Han is a world-leading researcher in data mining.
He is enthusiastic to describe the details of techniques. The course appears to be quite academic with only a single programming assignment, thus the material is hard to internalize.

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