Hierarchical clustering approaches for high-throughput data
The clustering landscape
Hierarchical vs. Flat clustering
Hierarchical clustering
Hierarchical clustering example
Hierarchical clustering
Top-down clustering
Bottom-up hierarchical clustering
Distance between two clusters
Haven’t We Already Seen This?
Differences between general clustering and phylogenetic inference
Complete-link vs. single-link distances
Updating distances efficiently
Effect of different linkage methods
Flat clustering from a hierarchical clustering
Naïve computational complexity
Computational Complexity
How to pick the right clustering algorithm?
Summary of clustering

How to pick the right clustering algorithm?
If you have a sense of what the right number of clusters are, K-means or Gaussian mixture models might be good
If you want to control for the extent of dissimilarity you should use hierarchical
Hierarchical clustering is deterministic
Always gives the same solution with the same distance metric
K-means and Gaussian mixture model are non-deterministic
We have talked about clustering of gene expression profiles
However clustering could be used to find groupings among more complex objects
All we need is to define the right distance metric