Enhancing Digital Forensic Analysis Using Memetic Algorithm Feature Selection Method for Document Clustering

Text clustering is an effective way that helps crime investigation through grouping of crime-related documents. This paper proposes a Memetic Algorithm Feature Selection (MAFS) approach to enhance the performance of document clustering algorithms used to partition crime reports and criminal news as well as some benchmark text datasets. Two clustering algorithms have been selected to demonstrate the effectiveness of the proposed MAFS method; they are the k-means and Spherical k-means (Spk). The reason behind using these clustering methods is to observe the performance of these algorithms before and after applying a hybrid FS that uses a Memetic scheme. The proposed MAFS method combines a Genetic Algorithm-based wrapper FS with the Relief-F filter. The performance evaluation was based on the clustering outcomes before and after applying the proposed MAFS method. The test results showed that the performance of both k-means and spk improved after the MAFS.