Consider the following eight points in a 2-dimensional space: {(2, 10); (2, 5); (8, 4); (5,...

   

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Consider the following eight points in a 2-dimensional space: {(2, 10); (2, 5); (8, 4); (5, 8); (7,5); (6, 4); (1, 2); (4, 9)}. Suppose we plan to use the Euclidean distance metric, and we are interested in clustering these points into 3 clusters. a. Plot the data points to see what appropriate clusters might be; b. Form three initial clusters with points {(2, 5), (5, 8), (4, 9)} as initial cluster centers; C. Use the K-Means clustering algorithm to get final three clusters. What are the resulting centers and resulting clusters? (K=3) Consider the following eight points in a 2-dimensional space: {(2, 10); (2, 5); (8, 4); (5, 8); (7,5); (6, 4); (1, 2); (4, 9)}. Suppose we plan to use the Euclidean distance metric, and we are interested in clustering these points into 3 clusters. a. Plot the data points to see what appropriate clusters might be; b. Form three initial clusters with points {(2, 5), (5, 8), (4, 9)} as initial cluster centers; C. Use the K-Means clustering algorithm to get final three clusters. What are the resulting centers and resulting clusters? (K=3)

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the KMeans algorithm applied to a set of eight data points in a 2dimensional space Heres how to solve the problem using Kmeans clustering with k3 3 clusters and Euclidean distance metric a Plotting th... View the full answer