10-601B Clustering
From Cohen Courses
This a pair of lectures used in the Syllabus for Machine Learning 10-601B in Spring 2016.
Slides
- ...
Readings
Mitchell 6.12 - a nice description of EM and k-means.
What You Should Know Afterward
You should know how to implement these methods, and what their relative advantages and disadvantages are.
- Overview of clustering
- Distance functions and similarity measures and their impact
- k-means algorithms (Lloyd's method, k-means++)
- Partitional clustering
- Hierarchical clustering
- How to chose k and what is the impact of large and small k's
