methods are effective but hard to use in practice as they are very slow. 36 

Indeed the universality of these measures comes at a price, as the com- 37 pression algorithm must be run at least n 2 times on n objects to derive a 38 distance matrix, slowing down the analysis. Furthermore, as these methods 39 are applied to raw data they cannot be tuned to increase their performance 40 on a given data type. We propose then to perform these tasks using the Fast 41 Compression Distance (FCD) recently defined in Cerra and Datcu (2012), 42 which provides superior performances with a reduced computational com- 43 plexity with respect to the NCD, and can be tuned according to the kind 44 of data at hand. In the case of natural texts, only FCD’s general settings 45 should be adjusted according to the language of the dataset, thus keeping 46 the desirable parameter-free approach typical of NCD. Applications to au- 47 thorship and plagiarism analysis are derived by extracting meaningful dictio- 48 naries directly from the strings representing the data instances and matching 49 them. The reported experiments show that improvements over traditional 50 compression-based analysis can be dramatic, and that the FCD could be- 51 come an important tool of easy usage for the automated analysis of texts, as 52 satisfactory results are achieved skipping any parameters setting step. The 53 only exception is an optional text preprocessing step which only needs to 54 be set once for documents of a given language, and does not depend on the 55 specific dataset. 56 

The paper is structured as follows. Section 2 introduces compression- 57 based similarity measures and the FCD, which will be validated in an array 58 

