Text Clustering for Digital Forensics Analysis

In the last decades digital forensics has become a prominent activity in modern investigations. Seized digital devices can provide precious information and evidences about facts and/or individuals on which the investigational activity is performed. Due to the complexity of this inquiring activity and to the large amount of the data to be analyzed, the choice of appropriate digital tools to support the investigation represents a central concern. In this paper an effective digital text analysis strategy, relying on clusteringbased text mining techniques, is introduced for investigational purposes. The proposed methodology is experimentally applied to the publicly available Enron dataset that well fits a plausible forensics analysis context.