a$)$ Given the Ratings of $5$ employees on $2$ different performance measurement indexes. Find clusters of similarly rated employees using K$-$means Algorithm, use Euclidean Distance as a distance measure and consider the number of clusters as $2.$ Choose "Stuart" and "Pavel" as initial centroids.

marks$]$

$\backslash$table$[[$Employee Name,Rating from Index $1,$Rating from Index $2],[$Jay$,0,1],[$Stuart$,3,0],[$Pavel$,2,4],[$Andrey$,2,1],[$Weifeng$,3,5]]$

b$)$ K$-$Means results largely depends on the initialization of k$-$centroids, it may be optimal or suboptimal clustering depending on the choice of initial centroids. Suggest ways to overcome this issue.

$[3$ marks$]$