Automatic keyword extraction with relational clustering and Levenshtein distances

Alternating cluster estimation (ACE) is a generalized clustering model. Relational ACE is a modification of ACE that can be used to cluster data which do not possess a clear numerical representation, but for which a meaningful relation matrix can be defined. For text data sets we define (pairwise) relation matrices based on the Levenshtein string distance (1966). Relational ACE with Levenshtein distances is applied to four different texts. The cluster centers represent typical words in the texts, so this algorithm can be used to automatically determine keywords.