Section - 1Answer any 4 out of the following questions.      4 * 5=201.How to choose initial cluster centroids in K-Means Clustering? Explain the different methods used for this purpose. Not Answered2.Differentiate between a line and a plane in two-dimensional and three-dimensional space. Given the equation of a line in slope-intercept form, y = mx + b, find the slope and y-intercept.Not Answered3.What is data normalization? Explain why it is important in kNN. Not Answered4.What are the different types of Hierarchical Clustering? Compare and contrast Agglomerative Hierarchical Clustering and Divisive Hierarchical Clustering.Not Answered5.Write any three advantages and disadvantages of logistic regression. With examplesNot Answered6.Why can't we do a classification problem using Regression? Discuss with relevant examples. Not Answered

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.

K means clustering algorithm clusters the data points based on:- Dependent and independent variables The eigen values Distance between the points and a cluster centre None of the above

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.”

Use K-means algorithm and Euclidean distance to cluster five data points (A4-A8) given below, into 3 clusters. The coordinates of the data points are:A1(2,8), A2(2,5), A3(1.2), A4(5,8), A5(7,3), A6(6,4), A7(8,4), A8(4,7)..Use A1, A2, A3 as initial centroids. For which situations K-mean clustering will give good results and when will it fail to produce good results?

Question 2Which option correctly orders the steps of k-means clustering?Re-cluster the data pointsChoose k random observations to calculate each cluster’s meanUpdate centroid to take cluster meanRepeat until centroids are constantCalculate data point distance to centroids1 point2, 1, 4, 5, 33, 5, 1, 4, 22, 3, 4, 5, 12, 5, 3, 1, 4