Clustering using hierarchical techniques

Hierarchical clustering techniques approach the analysis a bit differently than k-means clustering. Instead of working with a predetermined number of centers and iterating to find membership, hierarchical techniques continually pair or split data into clusters based on similarity (distance). There are two different approaches:

  • Divisive clustering: This begins with all the data in a single cluster and then splits it and all subsequent clusters until each data point is its own individual cluster
  • Agglomerative clustering: This begins with each individual data point and pairs them together in a hierarchy until there is just one cluster

In this section, you will learn and use agglomerative hierarchical clustering. ...

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