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Text mining with emergent self organizing maps and multi-dimensional scaling: A comparative study on domestic violence
Poelmans, Jonas Van Hulle, Marc M Viaene, Stijn Elzinga, Paul Dedene, Guido
Poelmans, Jonas
Van Hulle, Marc M
Viaene, Stijn
Elzinga, Paul
Dedene, Guido
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Publication Type
Journal article with impact factor
Editor
Supervisor
Publication Year
2011-06
Journal
Applied Soft Computing
Book
Publication Volume
11
Publication Issue
4
Publication Begin page
3870
Publication End page
3876
Publication Number of pages
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Abstract
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.
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Keywords
46 Information and Computing Sciences, 4903 Numerical and Computational Mathematics, 49 Mathematical Sciences, 4602 Artificial Intelligence