Consider the data found in the following file. In this problem, we will define total clustering error (TCE) as the sum of the individual distances of each point to its respective center as determined by the clustering algorithm: TCE= K Nj d (y, Hj) j=1 i=1 where y represents the ith point in cluster j and ; represents the cluster mean for cluster j, where cluster j has Nj points in it, and d (a, b) represents the squared L2 distance between vectors a and b. CleanClusters.csv Ignore the point values listed for this problem. Practice problems in this subsection will be worth a total of 2.5 points. Calculating Total Clustering Error 3 points possible (graded) Using K-Means, cluster the data using K = 3 means. What is the total clustering error under this choice of K? Round to the nearest unit. 129 88 69 45 43 38 37 Using K-Means, cluster the data using K = 5 means. What is the total clustering error under this choice of K? Round to the nearest unit. O 129 88 69 45 43 38 37 Using K-Means, cluster the data using K = 10 means. What is the total clustering error under this choice of K? Round to the nearest unit. O 129 88 69 45 43 38 88 37 Create a scatterplot of the data. Does the value of K that aligns with the number of clusters in the data correspond to the value of K that minimizes the total clustering error? Yes No