Text mining with emergent self organizing maps and multi-dimensional scaling: a comparative study on domestic violence

In this paper we compare the usability of ESOM and MDS as text exploration instruments in police investigations. We combine them with traditional classification instruments such as the SVM and Naïve Bayes. We perform a case of real-life data mining using a dataset consisting of police reports describing a wide range of violent incidents that occurred during the year 2007 in the Amsterdam-Amstelland police region (The Netherlands). We compare the possibilities offered by the ESOM and MDS for iteratively enriching our feature set, discovering confusing situations, faulty case labelings and significantly improving the classification accuracy. The results of our research are currently operational in the Amsterdam-Amstelland police region for upgrading the employed domestic violence definition, for improving the training of police officers and for developing a highly accurate and comprehensible case triage model.