Agglomerative Hierarchical clustering Technique: In this technique, initially each data point is considered as an individual cluster. At each iteration, the similar clusters merge with other clusters until one cluster or K clusters are formed.
The basic algorithm of Agglomerative is straight forward.
Compute the proximity matrix Let each data point be a cluster Repeat: Merge the two closest clusters and update the proximity matrix Until only a single cluster remains Key operation is the computation of the proximity of two clusters To understand better let’s see a pictorial representation of the Agglomerative Hierarchical clustering Technique. Lets say we have six data points {A,B,C,D,E,F}.
Step- 1: In the initial step, we calculate the proximity of individual points and consider all the six data points as individual clusters as shown in the image below.
Agglomerative Hierarchical Clustering Technique
Step- 2: In step two, similar clusters are merged together and formed as a single cluster. Let’s consider B,C, and D,E are similar clusters that are merged in step two. Now, we’re left with four clusters which are A, BC, DE, F.
Step- 3: We again calculate the proximity of new clusters and merge the similar clusters to form new clusters A, BC, DEF.
Step- 4: Calculate the proximity of the new clusters. The clusters DEF and BC are similar and merged together to form a new cluster. We’re now left with two clusters A, BCDEF.
Step- 5: Finally, all the clusters are merged together and form a single cluster.
The Hierarchical clustering Technique can be visualized using a Dendrogram. A Dendrogram is a tree-like diagram that records the sequences of merges or splits.
REFER TO : https://cs.wmich.edu/alfuqaha/summer14/cs6530/lectures/ClusteringAnalysis.pdf