Mining the crime survey to support crime profiling

Conference paper

Wu, J, Meziane, F., Mohamad Saraee, Aspin, R and Hope, T 2016. Mining the crime survey to support crime profiling. 2016 International Workshop on Computational Intelligence for Multimedia Understanding (IWCIM). Reggio Calabria, Italy 27 - 28 Oct 2016 IEEE Xplore. https://doi.org/10.1109/iwcim.2016.7801189
AuthorsWu, J, Meziane, F., Mohamad Saraee, Aspin, R and Hope, T
TypeConference paper
Abstract

Crime surveys are conducted to record crimes by the Office for National Statistics (ONS) in the United Kingdom every year. They contain rich information about crime. They record the crimes that are not reported to the police. However, their exploitation for gaining a better understanding of crime activities is limited. When used, traditional statistical models and descriptive statistics are adopted. The data they contain is very complex and changes from one year to the other. In this paper, we report the preprocessing activities that were performed on survey data to allow their use with data mining models. We reported the results of early analysis of the survey data using decision trees and the users' interpretation of these results.

KeywordsClassification; ; Feature extraction; ; Plantar fascia (PF); ; Radial Basic Function Neural Network (RBF-NN); ; Segmentation; ; Speckle noise; ; Ultrasound (US)
Year2016
Conference2016 International Workshop on Computational Intelligence for Multimedia Understanding (IWCIM)
JournalInternational Workshop on Computational Intelligence for Multimedia Understanding (IWCIM)
PublisherIEEE Xplore
ISSN0895-6111
1879-0771
Digital Object Identifier (DOI)https://doi.org/10.1109/iwcim.2016.7801189
Web address (URL)http://dx.doi.org/10.1109/IWCIM.2016.7801189
Journal citationVol 56, pp. 60 - 73
ISBN978-1-5090-5524-1
Web address (URL) of conference proceedingshttps://ieeexplore.ieee.org/xpl/conhome/7786725/proceeding
Output statusPublished
Publication datesOct 2016
Online29 Dec 2016
Publication process dates
Deposited05 Jun 2023
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