Question 1: Suppose that the data for analysis includes the attribute age. The age values for the data tuples are (in increasing order) 13,15,16,16,19,20,20,21,22,22,25,25,25,25,30,33,33,35,35, 35,35,36,40,45,46,52,70. Answer the following: (i) Use min-max normalization to transform the value 35 for age onto the range [0.0,1.0]. Page 2 of 4 CSC 493/579 - Fall 2022 Final Study Guide (ii) Use z-score normalization to transform the value 35 for age, where the standard deviation of age is 12.94 years. (iii) Use normalization by decimal scaling to transform the value 35 for age. (iv) Comment on which method you would prefer to use for the given data, giving reasons as to why. Question 2: Suppose that the data mining task is to cluster the following eight points (with ( x,y) representing location) into three clusters: A1(2,10),A2(2,5),A3(8,4),B1(5,8),B2(7,5),B3(6,4),C1(1,2),C2(4,9). The distance function is Euclidean Distance. Suppose initially we assign A1,B1 and C1 as the center of each cluster, respectively. Use the k-means algorithm to show only (i) The three cluster centers after the first-round execution (ii) The final three clusters Question 3: Given two objects represented by the vectors and : (i) Compute the Euclidean distance between the two objects. (ii) Computer the Manhattan distance between the two objects. (iii) Computer the Minkowski distance between the two objects, using q=3. Question 4: Describe each of the following clustering algorithms in terms of the following criteria: (i) input parameters that must be specified; and (ii) limitations. 1. k-means 2. k-medoids 3. DIANA 4. DBSCAN Question 5: Given the dataset of 14 samples, compute the "Gain" for each of the attributes \{age, income, student, credit_rating }, and list them in the order of importance. Question 1: Suppose that the data for analysis includes the attribute age. The age values for the data tuples are (in increasing order) 13,15,16,16,19,20,20,21,22,22,25,25,25,25,30,33,33,35,35, 35,35,36,40,45,46,52,70. Answer the following: (i) Use min-max normalization to transform the value 35 for age onto the range [0.0,1.0]. Page 2 of 4 CSC 493/579 - Fall 2022 Final Study Guide (ii) Use z-score normalization to transform the value 35 for age, where the standard deviation of age is 12.94 years. (iii) Use normalization by decimal scaling to transform the value 35 for age. (iv) Comment on which method you would prefer to use for the given data, giving reasons as to why. Question 2: Suppose that the data mining task is to cluster the following eight points (with ( x,y) representing location) into three clusters: A1(2,10),A2(2,5),A3(8,4),B1(5,8),B2(7,5),B3(6,4),C1(1,2),C2(4,9). The distance function is Euclidean Distance. Suppose initially we assign A1,B1 and C1 as the center of each cluster, respectively. Use the k-means algorithm to show only (i) The three cluster centers after the first-round execution (ii) The final three clusters Question 3: Given two objects represented by the vectors and : (i) Compute the Euclidean distance between the two objects. (ii) Computer the Manhattan distance between the two objects. (iii) Computer the Minkowski distance between the two objects, using q=3. Question 4: Describe each of the following clustering algorithms in terms of the following criteria: (i) input parameters that must be specified; and (ii) limitations. 1. k-means 2. k-medoids 3. DIANA 4. DBSCAN Question 5: Given the dataset of 14 samples, compute the "Gain" for each of the attributes \{age, income, student, credit_rating }, and list them in the order of importance
