Use the similarity matrix in the following table to perform single link (or MIN) hierarchical clustering. Show your results by drawing a nested cluster diagram or dendrogram. The nested cluster diagram (or dendrogram) should clearly show the order in which the points are merged. P1 P2 P3 P4 P5 P1 1.00 0.20 0.45 0.25 0.35 P2 0.20 1.00 0.64 0.47 0.98 P3 0.45 0.64 1.00 0.44 0.85 P4 0.25 0.47 0.44 1.00 0.76 P5 0.35 0.98 0.85 0.76 1.00
Added by Victoria H.
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00). The largest similarity is 0.98 between P2 and P5, so these two points will be merged first to form cluster (P2, P5). Show more…
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Problem 2 (10 points). Use the similarity matrix in the following table to perform single and complete link hierarchical clustering. Show your results by drawing a dendrogram. The dendrogram should show the order in which the points are merged (Coding is not mandatory here.) | | p1 | p2 | p3 | p4 | p5 | |---|---|---|---|---|---| | p1 | 1.00 | 0.10 | 0.41 | 0.55 | 0.35 | | p2 | 0.10 | 1.00 | 0.64 | 0.47 | 0.98 | | 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 |
Aarti K.
'Problem 3 (20 points) Use the similarity matrix as follows to perform single and complete link hierarchical clustering: Show your results by drawing dendrogram with R The dendrogram should clearly show the order in which the points are merged. Please show your R code: pl p2 p3 p4 p5 pl 1.00 0.10 0.41 0.55 0.35 p2 0.10 1.00 0.64 0.47 0.98 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'
Sri K.
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