1. A computer-implemented method for clustering text, comprising: representing at least one text in a set of texts as a dimensional vector of words; representing an other text in the set of texts as a dimensional vector of words; determining a dot-product of the dimensional vector of the other text and the dimensional vector of the at least one text; comparing the dot-product to a threshold, wherein the threshold comprises an upper bound of a value in a range from zero to one that represents a cosine similarity between the other text and the at least one text; if the dot-product exceeds the threshold, determining the at least one text to be the good nearest neighbor of the other text; clustering the other text in a cluster; and clustering the at least one text determined to be the good nearest neighbor of the other text in the cluster.