Difference between revisions of "10-601B Clustering"
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* Overview of clustering | * Overview of clustering | ||
* Distance functions and similarity measures and their impact | * 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 | * How to chose k and what is the impact of large and small k's | ||
| − | + | <!-- * Differences between GM and K-means --> | |
| − | * Differences between GM and K-means | ||
Revision as of 11:03, 14 January 2016
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
