In computer forensic analysis, hundreds of thousands of files are usually examined. Much of the data in those files consists of unstructured text, whose analysis by computer examiners is difficult to be performed. In this context, automated methods of analysis are of great interest. In particular, algorithms for clustering documents can facilitate the discovery of new and useful knowledge from the documents under analysis. We present an approach that applies document clustering algorithms to forensic analysis of computers seized in police investigations. Our experiments show that the hierarchical and NB ( naïve bayes) provide the best results for our application domain. Finally, we also present and discuss several practical results that can be useful for researchers and practitioners of forensic computing.