Traditional agglomerative hierarchical clustering routines merge two clusters at each step. Does it seem likely that such an approach accurately captures the (nested) cluster structure of a set of data points? If not, explain how you might postprocess the data to obtain a more accurate view of the cluster structure.
What will be an ideal response?
(a) Such an approach does not accurately capture the nested cluster struc-
ture of the data. For example, consider a set of three clusters, each of
which has two, three, and four subclusters, respectively. An ideal hi-
erarchical clustering would have three branches from the root—one to
each of the three main clusters—and then two, three, and four branches
from each of these clusters, respectively. A traditional agglomerative
approach cannot produce such a structure.
(b) The simplest type of postprocessing would attempt to flatten the hier-
archical clustering by moving clusters up the tree.
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What will be an ideal response?
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What will be an ideal response?