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. ...