3. Write your own RStudio code to implement the k-means algorithm, consider the following data set consisting of the scores of two variables on each of seven individuals: Subject A B 1 1.0 1.0 2 1.5 2.0 3 3.0 4.0 4 5.0 7.0 5 3.5 5.0 6 4.5 5.0 7 3.5 4.5 This data set is to be grouped into two clusters. As a first step in finding a sensible initial partition, let the A & B values of the two individuals furthest apart (using the Euclidean distance measure), assume the initial cluster means: Mean Individual Vector (centroid) Group 1 1 (1.0, 1.0) Group 2 4 (5.0, 7.0) Apply your RStudio code to assigned to cluster with the closest centroid. Now the initial partition has changed, and the two clusters at this stage having the following characteristics: Mean Individual Vector (centroid) Cluster 1 Cluster 2 1, 2, 3 (1.8, 2.3) 4,5,6,7 (4.1, 5.4) Use your RStudio code to verify the above results.