Hierarchical Methods

Hierarchical clustering methods can be further classified as either agglomerative or divisive, depending on whether the hierarchical decomposition is formed in a buttom-up (merging) or top-down (splitting) fashion.

Types of Hierarchical Clustering Methods :

There are two types of hierarchical clustering methods which are as follows:

• Agglomerative Hierarchical Clustering (AHC) :

AHC is a bottom-up clustering method where clusters have sub-clusters, which in turn have sub-clusters, etc. It begins by locating each object in its cluster and then combines these atomic clusters into larger and larger clusters until all the objects are in a single cluster or until it satisfies specific termination condition. Most hierarchical clustering methods are applied to this type. They are distinct only in their definition of between-cluster similarity.

• Divisive Hierarchical Clustering (DHC):

DHC is a top-down approach and is less generally used. It works in similar methods to agglomerative clustering but in the opposite direction. This method begins with a single cluster including all objects, and then successively splits resulting clusters until only clusters of single objects remain or until it satisfies specific termination condition, including a desired number of clusters is obtained or the distance Between two closest clusters is above a specific threshold distance.