Step 1: Choose the number of clusters k. … Step 2: Select k random points from the data as centroids. … Step 3: Assign all the points to the closest cluster centroid. … Step 4: Recompute the centroids of newly formed clusters. … Step 5: Repeat steps 3 and 4.

How do you find K mean?

1. Step 1: Choose the number of clusters k. …
2. Step 2: Select k random points from the data as centroids. …
3. Step 3: Assign all the points to the closest cluster centroid. …
4. Step 4: Recompute the centroids of newly formed clusters. …
5. Step 5: Repeat steps 3 and 4.

How do you determine the value of K in K-means clustering?

There is a popular method known as elbow method which is used to determine the optimal value of K to perform the K-Means Clustering Algorithm. The basic idea behind this method is that it plots the various values of cost with changing k. As the value of K increases, there will be fewer elements in the cluster.