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

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

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

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

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 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.”