K-Means-Clustering

I implemented the K-Means Clustering Model to Analyse the Data in Python and R

The steps I followed are :

• Choose the number K of the clusters
• Select at random K points , the centroids ( not necessarily from your dataset )
• Assign each data point to the closest centroid - That forms K cluster [ We basically take Eucleidan distance ]
• Compute and place the new centroid of each Cluster
• Reassign each data point to the new closest centroid -If any reassignment takes place go , to 4th step else END

Due to the K-Means initialization trap we have to use 'the' K-Means++

So we use the Within cluster Sum of Squares and use the Elbow method to select optimal number of cluster

to know abut the <- operator I used in R

It is a Interative process