a) Describe the differences between k-nearest neighbour and k-means clustering. For each approach, provide a real-world problem that can potentially be solved with the approach.

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a) Describe the differences between k-nearest neighbour and k-means
clustering. For each approach, provide a real-world problem that can
potentially be solved with the approach.
b) Given the data in Table 1 where and
are the input features of
each data point. Using k-means clustering with d4 and d as the initial
centroids, calculate the next centroids after one update.
Based on the values of the new centroids calculated, should the k-
means clustering process be terminated? Why or why not?
Table 1: 10 data points with
and 2 as input features
I1
I₂
d₁
0.21
0.89
d₂
0.22
0.83
da 0.74 0.25
da 0.83 0.69
ds 0.74 0.63
de
0.35
0.28
d7 0.76 0.89
de 0.54
0.45
de 0.44 0.94
do 0.34 0.87
Transcribed Image Text:a) Describe the differences between k-nearest neighbour and k-means clustering. For each approach, provide a real-world problem that can potentially be solved with the approach. b) Given the data in Table 1 where and are the input features of each data point. Using k-means clustering with d4 and d as the initial centroids, calculate the next centroids after one update. Based on the values of the new centroids calculated, should the k- means clustering process be terminated? Why or why not? Table 1: 10 data points with and 2 as input features I1 I₂ d₁ 0.21 0.89 d₂ 0.22 0.83 da 0.74 0.25 da 0.83 0.69 ds 0.74 0.63 de 0.35 0.28 d7 0.76 0.89 de 0.54 0.45 de 0.44 0.94 do 0.34 0.87
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