However , K - means clustering is computationally intensive and it often does not generate the optimal number of centers .
One approach first uses K - means clustering to find cluster centers which are then used as the centers for the RBF functions .
K - means clustering is computationally intensive and it often does not generate the optimal number of centers .
One approach first uses K - means clustering to find cluster centers which are then used as the centers for the RBF functions .