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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

down

It is a Interative process