Clustering - Lesson Plan

Learning outcomes

• Understand goals of clustering (separate different objects and keep similar ones together)
• Understand the difference between datapoint and features
• Able to apply Lloyds algorithm for k-means clustering: runtime and convergence of k-means algorithm
• Able to do prediction using clustering algorithm.
• Pitfalls of k-means clustering algorithm
• Understand the need to evaluate cluster goodness
• Understand difference between hierarchical and k-means clustering

Class activities

• Discuss why clustering is necessary
• Discuss Lloyd's k-means clustering algorithm
• Solve hierarchical clustering algorithm 

Resources

http://home.deib.polimi.it/matteucc/Clustering/tutorial_html/AppletH.html Links to an external site.

https://www.naftaliharris.com/blog/visualizing-k-means-clustering Links to an external site.