The k-means algorithm is based on the (strong) initial condition to decide the number of clusters through the assignment of k initial centroids or means:
Then the distance between each sample and each centroid is computed and the sample is assigned to the cluster where the distance is minimum. This approach is often called minimizing the inertia of the clusters, which is defined as follows:
The process is iterative—once all the samples have been processed, a new set of centroids K(1) is computed (now considering the actual elements ...
