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

Authors:
Jonas Poelmans;Marc M. Van Hulle;Stijn Viaene;Paul Elzinga;Guido Dedene
Affiliations:
K.U. Leuven, Faculty of Business and Economics, Naamsestraat 69, 3000 Leuven, Belgium;K.U. Leuven, Laboratorium voor Neuro-en Psychofysiologie, Campus Gasthuisberg O&N2, Bus 1021, Herestraat 49, 3000 Leuven, Belgium;K.U. Leuven, Faculty of Business and Economics, Naamsestraat 69, 3000 Leuven, Belgium and Vlerick Leuven Gent Management School, Vlamingenstraat 83, 3000 Leuven, Belgium;Amsterdam-Amstelland Police, James Wattstraat 84, 1000 CG Amsterdam, The Netherlands;K.U. Leuven, Faculty of Business and Economics, Naamsestraat 69, 3000 Leuven, Belgium and Universiteit van Amsterdam Business School, Roetersstraat 11, 1018 WB Amsterdam, The Netherlands
Venue:
Applied Soft Computing
Year:
2011

Citing 6
Cited 2

The nature of statistical learning theory

The nature of statistical learning theory
Faithful Representations and Topographic Maps: From Distortion- to Information-Based Self-Organization

Faithful Representations and Topographic Maps: From Distortion- to Information-Based Self-Organization
Minimum Redundancy Feature Selection from Microarray Gene Expression Data

CSB '03 Proceedings of the IEEE Computer Society Conference on Bioinformatics
Feature Selection Based on Mutual Information: Criteria of Max-Dependency, Max-Relevance, and Min-Redundancy

IEEE Transactions on Pattern Analysis and Machine Intelligence
Tapping the power of text mining

Communications of the ACM - Privacy and security in highly dynamic systems
A comparison of methods for multiclass support vector machines

IEEE Transactions on Neural Networks

Growing Self-Organizing Map with cross insert for mixed-type data clustering

Applied Soft Computing
Exploring Users' Preferences in a Fuzzy Setting

Electronic Notes in Theoretical Computer Science (ENTCS)

Quantified Score

Hi-index	0.00

Visualization

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 Naive 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.