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Contextual Visualization of Crime Matching Through Interactive Clustering and Bayesian Theory

  • Nadeem QaziEmail author
  • B. L. William Wong
Chapter
Part of the Security Informatics and Law Enforcement book series (SILE)

Abstract

Police and law enforcement agencies perform social media analysis to gain a better understanding of criminal social networks structures and to identify potential criminal activities. The use of data mining techniques in social media analysis, however, faces issues and challenges such as linkage-based structural analysis, association extraction, community or group detection, behaviour and mood analysis, sentiment analysis and dynamic analysis of streaming networks. This chapter describes the extension of our developed framework and propose an association model for extracting multilevel associations based on associative questioning. We also describe data mining techniques used to visualize these associations through a 2D crime cluster space. The developed framework provides a complete data analytic solution towards identifying and understanding associations between crime entities and thus expedites the crime matching process.

Keywords

Crime matching Crime clustering Criminal analysis Social media analysis Data mining Associative questioning Methodology 

Notes

Acknowledgements

The research leading to the results reported here has received funding from the European Union Seventh Framework Programme through Project VALCRI, European Commission Grant Agreement N FP7-IP-608142, awarded to Middlesex University and partners.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Mechanical, Aerospace, and Civil EngineeringCollege of Engineering, Design and Physical Sciences, Brunel UniversityLondonUK
  2. 2.Middlesex UniversityLondonUK

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