Fuzzy Clustering based Methodology for Multidimensional Data Analysis in Computational Forensic Domain

Abstract

As interdisciplinary domain requiring advanced and innovative methodologies, the computational forensics domain is characterized by data being, simultaneously, large scaled and uncertain, multidimensional and approximate. Forensic domain experts, trained to discover hidden pattern from crime data, are limited in their analysis without the assistance of a computational intelligence approach. In this paper, a methodology and an automatic procedure, based on fuzzy set theory and designed to infer precise and intuitive expert-system-like rules from original forensic data, is described. The main steps of the methodology are detailed, as well as the experiments conducted on forensic data sets - both simulated data and real data, representing robberies and residential burglaries.