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UGC-NET | NTA UGC NET 2019 June – II | Question 29

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  • Last Updated : 03 Dec, 2021
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K-mean clustering algorithm has clustered the given 8 observations into 3 clusters after 1st iteration as follows:

C1 : {(3,3), (5,5), (7,7)}
C2 : {(0,6), (6,0), (3,0)}
C3 : {(8,8),(4,4)}

What will be the Manhattan distance for observation (4,4) from cluster centroid C1 in second iteration?
(A) 2
(B) √2
(C) 0
(D) 18


Answer: (A)

Explanation: Manhattan distance captures the distance between two points by aggregating the pairwise absolute difference between each variable while Euclidean distance captures the same by aggregating the squared difference in each variable.

So the new position of cluster centroid C1 after the 1st iteration will be (13(3+5+7),13(3+5+7))=(5,5)
The Manhattan distance between (4,4) and (5,5) is |4−5|+|4−5|=2

Ans : Option A

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