Regular space clustering threshold Often, one wants to keep as many features as possible so as not to miss anything important, but this can lead to a very high dimensional space to do clustering in. Euclidean distance used in regular space clustering can be useless in very high dimensional space. We really liked our choice of 200 CVs in the DDR1 case. On the other hand, the threshold in the regular space clustering mostly controls the number of cluster centers picked. Often, when sampling power is sufficient, the user is encouraged to pick a smaller threshold to get more cluster centers. When computation power is limited, a smaller number is also good enough, but some representative structures might be missed.