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

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.

What is the main objective of the K-Means algorithm?Answer areaTo minimize the sum of squared distances between points and their respective cluster centroidsTo maximize the distance between different clustersTo minimize the number of clustersTo maximize the variance within each cluster

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.

What is a disadvantage of K-Means clustering?Answer areaIt is easy to implementIt always converges to the global optimumIt is sensitive to the initial placement of centroidsIt works well with all shapes of clusters

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.