Clustering Approaches

There are two types of clustering:

• Flat
• Hierarchical

In flat clustering, we specify the number of clusters we would like the machine to find. One example of flat clustering is the k-means algorithm, where k specifies the number of clusters we would like the algorithm to use.

In hierarchical clustering, however, the machine learning algorithm itself finds out the number of clusters that are needed.

Hierarchical clustering also has two approaches:

• Agglomerative or bottom-up hierarchical clustering treats each point as a cluster to begin with. Then, the closest clusters are grouped together. The grouping is repeated until we reach a single cluster with every data point.
• Divisive or top-down hierarchical clustering treats data points as if they were all in one single cluster at the start. Then the cluster is divided into smaller clusters by choosing the furthest data points. The splitting is repeated until each data point becomes...