Difference between revisions of "10-601B Clustering"

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=== Slides ===
 
=== Slides ===
  
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* ...
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* [http://curtis.ml.cmu.edu/w/courses/images/5/5d/Clustering.pdf Slides in pdf]
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* [http://curtis.ml.cmu.edu/w/courses/images/f/fb/Clustering.pptx Slides in ppt]
  
 
=== Readings ===
 
=== Readings ===
  
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Mitchell 6.12 -  a nice description of EM and k-means.
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* Murphy 25.5
  
 
=== What You Should Know Afterward ===
 
=== What You Should Know Afterward ===
  
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You should know how to implement these methods, and what their relative advantages and disadvantages are.
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*Partitional Clustering. k-means and k-means ++
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* Overview of clustering
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** Lloyd’s method
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* Distance functions and similarity measures and their impact
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** Initialization techniques (random, furthest traversal, k-means++)
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* k-means algorithms (Lloyd's method, k-means++)
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* Partitional clustering
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* Hierarchical Clustering.
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* Hierarchical clustering
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** Single linkage, Complete linkage
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* How to chose k and what is the impact of large and small k's
 
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<!-- * Differences between GM and K-means -->
 

Latest revision as of 10:24, 8 March 2016

This a pair of lectures used in the Syllabus for Machine Learning 10-601B in Spring 2016.

Slides

Readings

  • Murphy 25.5

What You Should Know Afterward

  • Partitional Clustering. k-means and k-means ++
    • Lloyd’s method
    • Initialization techniques (random, furthest traversal, k-means++)
  • Hierarchical Clustering.
    • Single linkage, Complete linkage
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