Algorithm 1: Optimized K-means clustering method

Input:

X = consists of a total n number of data items.

C = required clusters

Output:

A complete set of C clusters

Steps:

1: Choose C data items as initial centroids from X randomly.

2: Repeat

3: Associate each data item to the closest available centroid

4: Mean value calculation for every cluster

5: Continue until it meets the convergence criteria.