23. More on Hierarchical Clustering (Part-2) | Machine Learning with Python | Tech2Teach
In this video, we’ll be covering Hierarchical
clustering. So let’s get started.
Let’s look at this chart. An international team of scientists, led by
UCLA biologists, used this dendrogram to report genetic data from more than 900 dogs from
85 breeds -- and more than 200 wild gray wolves worldwide, including populations from North
America, Europe, the Middle East, and East Asia.
They used molecular genetic techniques to analyze more than 48,000 genetic markers.
This diagram, shows hierarchical clustering of these animals based on the similarity in
their genetic data. Hierarchical clustering algorithms build a
hierarchy of clusters where each node is a cluster consisting of the clusters of its
daughter nodes.
Strategies for hierarchical clustering generally fall into two types: Divisive and Agglomerative.
Divisive is top-down, so you start with all observations in a large cluster and break
it down into smaller pieces. Think about divisive as "dividing" the cluster.
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