14 yields an accuracy of 90%. For FCD only two texts, L’Asino and Discorsi 203 sopra la prima deca di Tito Livio , both by Niccol´ o Machiavelli, are incor- 204 rectly assigned respectively to Dante and Guicciardini, but these errors may 205 be justified: the former is a poem strongly influenced by Dante (Caesar, 206 1989), while the latter was found similar to a collection of critical notes on 207 the very Discorsi compiled by Guicciardini, who was Machiavelli’s friend 208 (Machiavelli et al., 2002). The N-grams-based method also assigns incor- 209 rectly Guicciardini’s notes and a Dante’s poem to Machiavelli, among others 210 misclassifications. 211 We also compared our results with an array of other compression-based 212 similarity measures (Table 3): our results outperform both the Ziv-Merhav 213 distance (Pereira Coutinho and Figueiredo, 2005) and the relative entropy as 214 described in Benedetto et al. (2002), while the algorithmic Kullback-Leibler 215 divergence (Cerra and Datcu, 2011) obtains the same results in a consider- 216 ably higher running time. Accuracy for the NCD method using an array 217 of linear compressors ranged from the 93.3% obtained using the bzip2 com- 218 pressor to the 96.6% obtained with the blocksort compressor. Even though 219 accuracies are comparable and the dataset may be small to be statistically 220 meaningful, another advantage of FCD over NCD is the decrease in compu- 221 tational complexity. While for NCD it took 202 seconds to build a distance 222 matrix for the 90 pre-formatted texts using the zlib compressor (with no 223 appreciable variation when using other compressors), just 35 seconds were 224 needed on the same machine for the FCD: 10 to extract the dictionaries and 225