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3. Use the training data of Gauss2 example (Gauss2.train). (a). Run the K-means algorithm with K = 2 clusters and make a plot identifying the clusters. Also label the points with their known classes. = 2 clusters and make (b). Multiple all the x1 by 10. Run the K-means algorithm with K a plot identifying the clusters. Also label the points with their known classes. (c). Compare the class and cluster groupings for both plots in (a) and (b). Explain any differences between them. (d). Use agglomerative hierarchical clustering (hclust in the MASS library) to cluster the data, and divide them into 2 groups. You can use the function cutree to divide the clusters into 2 groups. Report on how the predicted cluster labels compare with actual cluster labels for each of the three methods for measuring distance between clusters, i.e. single linkage, complete linkage and average linkage. (e). Plot the dendrograms for the three methods in (d). What does the shape of the the dendrograms suggest about the performance of each method? (f). Top-down (or divisive) hierarchical clustering is another approach, in which the algo- rithm begins with all data points together and seeks to separate them into subgroups. This is implemented in R in the function diana which is part of the cluster library. Compare the top-down clustering with bottom-up methods in the previous question. Base the comparison on a classification of the points into 2 clusters.

3. Use the training data of Gauss2 example (Gauss2.train). (a). Run the K-means algorithm with K = 2 clusters and make a plot identifying the clusters. Also label the points with their known classes. = 2 clusters and make (b). Multiple all the x1 by 10. Run the K-means algorithm with K a plot identifying the clusters. Also label the points with their known classes. (c). Compare the class and cluster groupings for both plots in (a) and (b). Explain any differences between them. (d). Use agglomerative hierarchical clustering (hclust in the MASS library) to cluster the data, and divide them into 2 groups. You can use the function cutree to divide the clusters into 2 groups. Report on how the predicted cluster labels compare with actual cluster labels for each of the three methods for measuring distance between clusters, i.e. single linkage, complete linkage and average linkage. (e). Plot the dendrograms for the three methods in (d). What does the shape of the the dendrograms suggest about the performance of each method? (f). Top-down (or divisive) hierarchical clustering is another approach, in which the algo- rithm begins with all data points together and seeks to separate them into subgroups. This is implemented in R in the function diana which is part of the cluster library. Compare the top-down clustering with bottom-up methods in the previous question. Base the comparison on a classification of the points into 2 clusters.

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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3. Use the training data of Gauss2 example (Gauss2.train). (a). Run the K-means algorithm with K = 2 clusters and make a plot identifying the clusters. Also label the points with their known classes. = 2 clusters and make (b). Multiple all the x1 by 10. Run the K-means algorithm with K a plot identifying the clusters. Also label the points with their known classes. (c). Compare the class and cluster groupings for both plots in (a) and (b). Explain any differences between them. (d). Use agglomerative hierarchical clustering (hclust in the MASS library) to cluster the data, and divide them into 2 groups. You can use the function cutree to divide the clusters into 2 groups. Report on how the predicted cluster labels compare with actual cluster labels for each of the three methods for measuring distance between clusters, i.e. single linkage, complete linkage and average linkage. (e). Plot the dendrograms for the three methods in (d). What does the shape of the the dendrograms suggest about the performance of each method? (f). Top-down (or divisive) hierarchical clustering is another approach, in which the algo- rithm begins with all data points together and seeks to separate them into subgroups. This is implemented in R in the function diana which is part of the cluster library. Compare the top-down clustering with bottom-up methods in the previous question. Base the comparison on a classification of the points into 2 clusters.
Transcribed Image Text:3. Use the training data of Gauss2 example (Gauss2.train). (a). Run the K-means algorithm with K = 2 clusters and make a plot identifying the clusters. Also label the points with their known classes. = 2 clusters and make (b). Multiple all the x1 by 10. Run the K-means algorithm with K a plot identifying the clusters. Also label the points with their known classes. (c). Compare the class and cluster groupings for both plots in (a) and (b). Explain any differences between them. (d). Use agglomerative hierarchical clustering (hclust in the MASS library) to cluster the data, and divide them into 2 groups. You can use the function cutree to divide the clusters into 2 groups. Report on how the predicted cluster labels compare with actual cluster labels for each of the three methods for measuring distance between clusters, i.e. single linkage, complete linkage and average linkage. (e). Plot the dendrograms for the three methods in (d). What does the shape of the the dendrograms suggest about the performance of each method? (f). Top-down (or divisive) hierarchical clustering is another approach, in which the algo- rithm begins with all data points together and seeks to separate them into subgroups. This is implemented in R in the function diana which is part of the cluster library. Compare the top-down clustering with bottom-up methods in the previous question. Base the comparison on a classification of the points into 2 clusters.
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