Question: The K - means algorithm: Group of answer choices Requires the dimension of the feature space to be no bigger than the number of samples

The K-means algorithm:
Group of answer choices
Requires the dimension of the feature space to be no bigger than the number of samples
Has the smallest value of the objective function when K =1
Minimizes the within class variance for a given number of clusters
Converges to the global optimum if and only if the initial means are chosen as some of the samples themselves
None of the above

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