How can the sensitivity to the initial placement of centroids be addressed in the k-means algorithm?Select one:a.By using a hierarchical clustering approachb.By using a different clustering algorithmc.By using the k-means++ initialization methodd.By normalizing the data prior to clustering

How is a center point (centroid) picked for each cluster in k-means upon initialization? (select two)1 pointWe can create some random points as centroids of the clusters.We can randomly choose some observations out of the data set and use these observations as the initial means.We select the k points closest to the mean/median of the entire dataset.We can select it through correlation analysis.

Which of the following is ALWAYS TRUE about considerations regarding the implementation of k-means?I.  The k-means algorithm is sensitive to the starting positions of the initial centroid.II.  K-means can handle all types of variables.I onlyboth I and IIneither I nor IIII only

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.

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