You are given five different data points, p1, p2, p3, p4, p5. Use the similarity matrix in Figure 2 to perform complete link hierarchical clustering. a) For each of the four merging steps, show the updated proximity matrix of the clusters. b) Visualize the clustering solution using a dendrogram. The dendrogram should clearly show the order in which the points are merged, and the distances between the clusters merged. c) Which clusters are formed if we need to have two clusters? Note: while the proximity table that is given to you has similarities, you can easily transform them into distances if you compute d = 1 - s.

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You are given five different data points, p1, p2, p3, p4, p5. Use the similarity matrix in Figure 2 to
perform complete link hierarchical clustering.
a) For each of the four merging steps, show the updated proximity matrix of the clusters.
b) Visualize the clustering solution using a dendrogram. The dendrogram should clearly show the
order in which the points are merged, and the distances between the clusters merged.
c) Which clusters are formed if we need to have two clusters?
Note: while the proximity table that is given to you has similarities, you can easily transform them into
distances if you compute d = 1 - s.
p1
p2
p3
p4
p5
p1
1.00
0.10
0.41
0.55
0.35
p2
0.10
ulo
1.00
0.64
0.47
0.98
1
p3
0.41
0.64
1.00
0.44
0.85
p4
0.55
0.47
0.44
1.00
0.76
p5
0.35
0.98
0.85
0.76
1.00
Figure 2: Proximity matrix (similarity)
Transcribed Image Text:You are given five different data points, p1, p2, p3, p4, p5. Use the similarity matrix in Figure 2 to perform complete link hierarchical clustering. a) For each of the four merging steps, show the updated proximity matrix of the clusters. b) Visualize the clustering solution using a dendrogram. The dendrogram should clearly show the order in which the points are merged, and the distances between the clusters merged. c) Which clusters are formed if we need to have two clusters? Note: while the proximity table that is given to you has similarities, you can easily transform them into distances if you compute d = 1 - s. p1 p2 p3 p4 p5 p1 1.00 0.10 0.41 0.55 0.35 p2 0.10 ulo 1.00 0.64 0.47 0.98 1 p3 0.41 0.64 1.00 0.44 0.85 p4 0.55 0.47 0.44 1.00 0.76 p5 0.35 0.98 0.85 0.76 1.00 Figure 2: Proximity matrix (similarity)
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