1. A computer-implemented method for clustering data, comprising: obtaining a refined dataset, wherein the refined dataset comprises a plurality of refined datums, each of the refined datums comprises a refined mean and a refined variance; calculating a plurality of refined distance values of a plurality of refined datum pairs, wherein each of the refined datum pairs is formed by two of the refined datums, the refined distance values of the refined datum pairs are calculated using the refined means and the refined variances of the refined datums which form the refined datum pairs; selecting one of the refined datum pairs with the least distance value; clustering the refined datums, which form the selected refined datum pair, into a new datum; and replacing the refined datums, which form the selected refined datum pair, with the new datum.