In K-Means clustering, the algorithm iteratively updates the ______________ of each cluster until convergence.

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.

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.

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.

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.

What is the main goal of the k-means algorithm?Select one:a.To discover patterns or relationships within a datasetb.To partition a dataset into a specified number of clustersc.To classify data into predefined categoriesd.To predict the value of a continuous target variable