Implement an iterative algorithm $($k$-$means$)$ in Spark to calculate k$-$means for

a set of points that are in a file, a k$-$means algorithm in python. Do not use use K$-$means in MLib of Spark to solve the problem. Set the center points to k$=5.$

Follow this pattern:

Randomly assign a centroid to each of the k clusters $($k $=5).$

Calculate the distance of all observation to each of the k centroids

Assign observations to the closest centroid

Find the new location of the centroid by taking the mean of all the observations in each cluster

Repeat steps $3-5$ until the centroids do not change position

Note: You need a variable to decide when the K$-$means calculation is done $$ when

the amount the locations of the means changes between iterations is less than the variable. Set

the variable $=0\mathrm{.}1.$

Example of imput file $($an rdd$)$:

$[(7869,8696),(8676,-4746),(9484,112526),(-1827,5958),(987,900087),(18127,9383),(298,272),(91716,2827),(12625,92827)........]$