K-means Clustering Implementation

Implement a K-means clustering algorithm. Input: An integer k (number of clusters), a 2D list points (each element represents a coordinate point). Output: A 2D list containing k clusters, with each element corresponding to a data point within the cluster. Provide two methods for initial cluster centers: random selection and K-means++ initialization. The input data size is: 1 <= len(points), len(points[i]) <= 1000. Example: Input: k = 2, points = [[1, 2], [1, 4], [3, 4], [5, 2], [5, 6]] Output: [[[1, 2], [1, 4], [3, 4]], [[5, 2], [5, 6]]] Example Input 2 [[1, 2], [1, 4], [3, 4], [5, 2], [5, 6]]