a) Given are the two dimensional points A(2, 6), B(3, 5), C(4, 7), D(9, 2), E(5,10), F(11,7). Compute single-link bottom-up hierarchical clustering using the Euclidean Distance formula. Show the clusters representation in xy-plane and draw a dendrogram of it. a) Given are the points A = (2,3), B = (3,3), C = (3,2), D = (-2,5), E = (-3,-2), F = (-2,-2). Starting from initial clusters Cluster1 = {A} which contains only the point A and Cluster2 = {D} which contains only the point D, run the K-means clustering algorithm and report the final clusters. Use L1 distance as the distance between points which is given by d ((x1, y1), (x2, y2)) = | x1 – x2 | + | y1 – y2 |. Draw the points on a 2-D grid and check if the clusters make sense. You may stop the clustering process if it found that two iterations have the same clusters, otherwise, at least 4 iterations are required. Rubrics (Grading Criteria): Understanding: 30% Problem Solving with Working: 50% Result: 20%
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a) Given are the two dimensional points A(2, 6), B(3, 5), C(4, 7), D(9, 2), E(5,10), F(11,7). Compute single-link bottom-up hierarchical clustering using the Euclidean Distance formula. Show the clusters representation in xy-plane and draw a dendrogram of it. |
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a) Given are the points A = (2,3), B = (3,3), C = (3,2), D = (-2,5), E = (-3,-2), F = (-2,-2). Starting from initial clusters Cluster1 = {A} which contains only the point A and Cluster2 = {D} which contains only the point D, run the K-means clustering algorithm and report the final clusters. Use L1 distance as the distance between points which is given by d ((x1, y1), (x2, y2)) = | x1 – x2 | + | y1 – y2 |. Draw the points on a 2-D grid and check if the clusters make sense. You may stop the clustering process if it found that two iterations have the same clusters, otherwise, at least 4 iterations are required. |
Rubrics (Grading Criteria):
Understanding: 30%
Problem Solving with Working: 50%
Result: 20%
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