essays. We compare results with the hierarchical clustering derived from the 181 distance matrix obtained on the basis of NCD distances (Fig. 3), run with 182 the default blocksort compression algorithm provided by CompLearn: in this 183 case the misplacements of the documents is evident, as disputed works are 184 in general closer to Madison texts but are scattered throughout the tree. 185 The rise of interest in compression-based methods is in part due to the 187 concept of relative entropy as described in Benedetto et al. (2002), which 188 quantifies a distance between two isolated strings relying on information the- 189 oretical notions. In this work the authors succesfully perform clustering and 190 classification of documents: one of the considered problems is to automati- 191 cally recognize the authors of a collection comprising 90 texts of 11 known 192 Italian authors spanning the centuries XIII-XX, available at Onlus (2003). 193 Each text x was used as a query against the rest of the database, its clos- 194 est object y minimizing the relative entropy D ( x, y ) was retrieved, and x 195 was then assigned to the author of y . In the following experiment the same 196 procedure as Benedetto et al. (2002) and a dataset as close as possible have 197 been adopted, with each text x assigned to the author of the text y which 198 minimizes FCD ( x, y ). We compare our results with the ones obtained by 199 the Common N-grams (CNG) method proposed by Keˇselj et al. (2003) us- 200 ing the most relevant 500, 1000 and 1500 3-grams in Table 2. The FCD 201 finds the correct author in 97.8% of the cases, while the best n-grams setting 202 11 186 3.2. The Liber Liber dataset