When the parameter K for k-means clustering increases, what happens to the error?1 pointIt will decrease because the data points are less possible to be in the wrong cluster.It will increase because incorrectly classified points are further from the correct centroid.It will decrease because distance between data points and centroid will decrease.It might increase or decrease depending on if data points are closer to the centroid.

3.Question 3How can we gauge the performance of a k-means clustering model when ground truth is not available?1 pointTake the average of the distance between data points and their cluster centroids.Calculate the number of incorrectly classified observations in the training set.Determine the prediction accuracy on the test set.Calculate the R-squared value to measure model fit.4.Question 4When the parameter K for k-means clustering increases, what happens to the error?1 pointIt might increase or decrease depending on if data points are closer to the centroid.It will increase because incorrectly classified points are further from the correct centroid.It will decrease because distance between data points and centroid will decrease.It will decrease because the data points are less possible to be in the wrong cluster.5.Question 5Which of the following is true for partition-based clustering but not hierarchical nor density-based clustering algorithms?1 pointPartition-based clustering produces arbitrary shaped clusters.Partition-based clustering can handle spatial clusters and noisy data.Partition-based clustering produces sphere-like clusters. Partition-based clustering is a type of unsupervised learning algorithm.

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.

Question 1The objective of k-means clustering is:1 pointSeparate dissimilar samples and group similar onesMinimize the cost function via gradient descentYield the highest out of sample accuracyMaximize the number of correctly classified data points

Which of the following is NOT a limitation of the k-means algorithm?Select one:a.It is not affected by the scale of the variablesb.It may produce suboptimal results if the clusters are not sphericalc.It is sensitive to the initial placement of centroidsd.It requires the user to specify the number of clusters in advance

Consider the unlabeled data given below: You are now provided with two sets of center points: C1 = (1,2),(2,2),(0,-2) C2 = (0,-2)(4,2)(4.5,0) Would the K-Means algorithm with k = 3 converge to the same clusters when either C1 or C2 were used as the initial center points? If yes, explain your reasoning. If not, explain why not.