5. A computer-implemented method for clustering text, comprising: representing a text in a set of texts as a dimensional vector of words; representing each of one or more texts in the set of texts as a dimensional vector of words; for each of the one or more texts, determining a dot-product of the dimensional vector of the each of the one or more texts and the dimensional vector of the text; comparing the dot-product for each of the one or more texts to a similarity threshold; for each of the one or more texts, determining a measure of a number of words used both by the text and the each of the one or more texts; comparing the measure of the number of words for each of the one or more texts to an overlap threshold; if the similarity threshold exceeds the dot-product for each of the one or more texts and the overlap threshold exceeds the measure of the number of words for each of the one or more texts, determining the text not to be similar to the one or more texts in the set of texts; excluding the text determined not to be similar from clustering with the set of texts; determining at least one text of the one or more texts in the set of texts to be a good nearest neighbor of an other text of the one or more texts in the set of texts; 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.
5. A computer-implemented method for clustering text, comprising: representing a text in a set of texts as a dimensional vector of words; representing each of one or more texts in the set of texts as a dimensional vector of words; for each of the one or more texts, determining a dot-product of the dimensional vector of the each of the one or more texts and the dimensional vector of the text; comparing the dot-product for each of the one or more texts to a similarity threshold; for each of the one or more texts, determining a measure of a number of words used both by the text and the each of the one or more texts; comparing the measure of the number of words for each of the one or more texts to an overlap threshold; if the similarity threshold exceeds the dot-product for each of the one or more texts and the overlap threshold exceeds the measure of the number of words for each of the one or more texts, determining the text not to be similar to the one or more texts in the set of texts; excluding the text determined not to be similar from clustering with the set of texts; determining at least one text of the one or more texts in the set of texts to be a good nearest neighbor of an other text of the one or more texts in the set of texts; 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. 7. The method of claim 5 wherein the overlap threshold comprises a value in a range from zero to one that represents the measure of words used in common by the text and the each of the one or more texts.