Asked by donhoggs

# I have this practice in R in one of mt classes. the question below...

I have this practice in R in one of mt classes.

the question below is the practice for two weeks ago, can you please explain.

Given the matrix X whose rows represent different data points, you are asked to perform a k-means clustering on this dataset using the Euclidean distance as the distance function. Here k is chosen as 3. The

Euclidean distance d between a vector x and a vector y is defined as Eculidian Distance. The centers of 3 clusters were initialized as µ1 = (6.2, 3.2) (red),

µ2 = (6.6, 3.7) (green), µ3 = (6.5, 3.0) (blue).

X =

5.9 3.2

4.6 2.9

6.2 2.8

4.7 3.2

5.5 4.2

5.0 3.0

4.9 3.1

6.7 3.1

5.1 3.8

6.0 3.0

1. What's the center of the first cluster (red) after one iteration? (Answer in the format of [x1, x2], round

your results to three decimal places, same as problems 2 and 3)

2. What's the center of the second cluster (green) after two iteration?

3. What's the center of the third cluster (blue) when the clustering converges?

4. How many iterations are required for the clusters to converge?

Answered by edgardorada0002

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