Question

# 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?

Solved by verified expert

usce dui l

iscing elit. Nam lacinia pulvinar tortor nec facilisis. Pellentesque d

gue

sque dapi

ongue vel laoreet ac, dictum vitae odio. Donec aliquet. Lorem ipsum dolor si

gue

usce dui l

ur laoreet. Nam risus ante, dapibus a molestie consequat, ultrices ac magna. Fusce

gue

ipsum dolo

sum dolor sit amet, consectetur adipiscing

Explore over 16 million step-by-step answers from our library

Step-by-step explanation

usce dui l

molestie consequat, ultrices ac magna. Fusce dui lectus, congue vel laoreet ac, dictum vitae odio. Donec aliquet. Lorem ipsum dolor sit amet, consectetur adipiscing elit. Nam lacinia pulvinar tortor nec facilisis. Pellentesque dapibus efficitur laoreet. Nam risus ante, dapibus a molestie consequat, ultrices ac magna. Fusce dui lectus, congue vel laoreet ac, dictum vitae odio. Donec aliquet

gue

sque dapi

ng elit. Nam lacinia pulvinar tortor nec facilisis. Pellentesque dapibus efficitur laoreet. Nam risus ante, dapibus a molestie consequat, ultrices ac magna. Fusce dui lectus, congue vel laoreet ac, dictum

gue

usce dui l

risus ante, dapibus a molestie consequat, ultrices ac magna. Fusce dui lectus, congue vel laoreet ac, dictum vitae odio. Donec aliquet. Lorem ipsum dolor sit amet, consectetur adipiscing elit. Nam lacinia pulvinar tortor nec facilisis. Pellentesque dapibus efficitur laoreet. Nam risus ante, dapibus a molestie consequat, ultrices ac magna. Fusce dui lectus, congue vel laoreet ac, dictum vitae odio. Donec aliquet. Lorem ipsum dolor sit amet, consectetur adipiscing elit. Nam lacinia pulvinar tort

gue

ipsum dolo

o. Donec aliquet. Lorem ipsum dolor sit amet, consectetur adipiscing elit. Nam lacinia pulvinar tortor nec facilisis. Pel

gue

gue

gue

Student review
100% (1 rating)
Thorough explanation