For a specified value of k$,$ why is it recommended to implement the k$-$means clustering algorithm with multiple starts $($with each start having a different set of k randomly selected initial centroids$)?$

Group of answer choices

Multiple starts are the remedy to the k$-$means clustering algorithm's sensitivity to outliers.

Multiple starts of the k$-$means clustering algorithm are only necessary to cluster high$-$dimensional data.

The location of the initial k randomly selected centroids can have an impact on the final clusters obtained.

Because k$-$means clustering typically employs the Euclidean distance measure, multiple starts are necessary to appropriately cluster non$-$globular data.