Q. The K-means algorithm:

• (A) requires the dimension of the feature space to be no bigger than the number of samples
• (B) has the smallest value of the objective function when k = 1
• (C) minimizes the within class variance for a given number of clusters
• (D) converges to the global optimum if and only if the initial means are chosen as some of the samples themselves