about clustering

1. Agglomerative Hierarchical
clustering
The agglomerative hierarchical clustering algorithm is a popular example of
HCA. To group the datasets into clusters, it follows the bottom-up approach.
It means, this algorithm considers each dataset as a single cluster at the
beginning, and then start combining the closest pair of clusters together. It
does this until all the clusters are merged into a single cluster that contains all
the datasets.
Why hierarchical clustering?
APPLICATIONS OF
CLUSTERING

2. Step 1 Step 2
Step 4
Step 3

3. FLOW CHART

5. MATLAB CODE :

7. For finding the optimal number of clusters we need to :
1. Determine the largest vertical distance that doesn’t intersect any other
cluster.
2. Draw two horizontal lines at both extremes like A and B in above figure.
3. The optimal number of cluster = number of vertical lines going through
the horizontal lines.