Consider the following 1-D points (i.e., numbers): 0, 2, 4, 7, 10. Your task is to apply the standard k-means algorithm to partition these numbers into two clusters (i.e., k = 2). Use the following pseudo-code for this problem: Initialize the prototypes Repeat until "convergence" Assign each sample to the cluster of the closest prototype. Recalculate each prototype as the mean of all the samples belonging to that cluster. A common approach to initialize the prototypes (cluster representatives) is to use points that are most distant from one another. For the numbers here, this means that the initial prototypes p 1 and p 2 are at 0 and 10, respectively. Now follow the pseudo-code until convergence, and give the final clusters and their prototypes.