Question 2Which option correctly orders the steps of k-means clustering?Re-cluster the data pointsChoose k random observations to calculate each cluster’s meanUpdate centroid to take cluster meanRepeat until centroids are constantCalculate data point distance to centroids1 point2, 1, 4, 5, 33, 5, 1, 4, 22, 3, 4, 5, 12, 5, 3, 1, 4

Which of the following steps is NOT part of the K-means clustering algorithm? Randomly initialise K cluster centroids.Assign each observation to the nearest centroid.Update the cluster centroids based on the assigned observations. Compute the silhouette score for each observation.

Question 2Which statement describes better “the smarter initialization of K-mean clusters? 1 point“Draw a line between the data points to create 2 big clusters.” “After we find our centroids, we calculate the distance between all our data points.”“Pick one random point, as initial point, and for the second point, instead of picking it randomly, we prioritize by assigning the probability of the distance.” “We start by having two centroids as far as possible between each other.”

The following is ALWAYS TRUE about the k-means algorithm EXCEPTCentroids are recomputed for each newly defined cluster and data points are reassigned based on the proximity to the newly computed centroids.The k-means results to an equal number of data points per cluster.Convergence is reached when the computed centroids do not change or the centroids and the assigned points oscillate back and forth from one iteration to the next.The optimum number of clusters may be determined by examining the within sum of squares for different values of k.

The k-means clustering algorithm works by (Select one) A. iteratively improving the position of k centroids in the sample space until an optimal placement is found. B. starting with one point in the sample space, finding more points in the space within a neighborhood ℇ until no more points can be found, and then repeating this process for k-1 points. C. iteratively determining the Gaussian distribution (via its mean and standard deviation) of k clusters until the probabilities of all points in the sample space are maximized. D. pairing each point with another point such that their distance is minimized, and then repeating this process with larger groups of points until there are only k clusters remaining.

Question 1Which of the following statements best describes the iterative part of the K-means algorithm? 1 pointThe k-means algorithm assigns a number of clusters at random. The k-means algorithm adjusts the centroids to the new mean of each cluster, and then it keeps repeating this process until no example is assigned to another cluster.The k-means algorithm iteratively deletes outliers.The k-means algorithm iteratively calculates the distance from each point to the centroid of each cluster.