Clustering digital forensic string search output

This research comparatively evaluates four competing clustering algorithms for thematically clustering digital forensic text string search output, finding that the performance of k-means clustering can be greatly improved with a non-linear, non-centroid-based cluster and document navigation procedure, which has potential implications for digital forensic tools and use thereof.