Please solve quick and answer all parts. i will provide upvote.

1. Consider the following one-dimensional dataset: X = (1. 3. 4.5. 5. 6.5. 7. 8. 10). Apply K-means to partition the dataset into three clusters, assuming the initial cluster centers to be 1.0 and 10.0, respectively. How many updating steps are required before the algorithm converges? 2. Consider the following training vectors: X1 = (4,3), X2 = (4,4), X3 = (4,5), X4 = (0,4), X5 = (8,0), X6 = (3, 4), and X7 = (5, 4). (a) Apply K-means to partition the dataset into three clusters, assuming the initial cluster centers to be: T1 = x1, T2 = x4, and T3 Th 2 = x4, and T 3 = xs, How many updating steps are required before the algorithm converges? (b) Apply EM to partition the dataset into three clusters with the same centers initially. 1. Consider the following one-dimensional dataset: X = (1. 3. 4.5. 5. 6.5. 7. 8. 10). Apply K-means to partition the dataset into three clusters, assuming the initial cluster centers to be 1.0 and 10.0, respectively. How many updating steps are required before the algorithm converges? 2. Consider the following training vectors: X1 = (4,3), X2 = (4,4), X3 = (4,5), X4 = (0,4), X5 = (8,0), X6 = (3, 4), and X7 = (5, 4). (a) Apply K-means to partition the dataset into three clusters, assuming the initial cluster centers to be: T1 = x1, T2 = x4, and T3 Th 2 = x4, and T 3 = xs, How many updating steps are required before the algorithm converges? (b) Apply EM to partition the dataset into three clusters with the same centers initially