How K-means forms cluster:

K-means picks k number of points for each cluster known as centroids.

Each data point forms a cluster with the closest centroids i.e. k clusters.

Finds the centroid of each cluster based on existing cluster members. Here we have new centroids.

As we have new centroids, repeat step 2 and 3. Find the closest distance for each data point from new centroids and get associated with new k-clusters. Repeat this process until convergence occurs i.e. centroids does not change.