Auto-Contractive Maps and Minimal Spanning Tree: Organization of Complex Datasets on Criminal Behavior to Aid in the Deduction of Network Connectivity

Chapter

Abstract

Using the Metropolitan Police Service Central Drug Trafficking Database (London, UK), the Auto-Contractive Map neural network technology is applied to identify unknown associations among individuals contained within it. Whilst a database may contain associations of known individuals, it may be mined to identify associations that are altogether unknown and unexpected. It is shown that individuals can be identified who belong to the same gang or drug trafficking circle. The results produce a profile that is mathematically justified and devoid of any political involvement. Key to this analysis is the organization of the datasets such that a meaningful analysis and interpretation can be made. This organization is described and illustrated using the drug trafficking data.

Keywords

Drug Trafficking Police Operation Previous Offense Drug Seizure Previous Arrest 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Kruskal, J. B. (1956). On the shortest spanning subtree of a graph and the traveling salesman problem. Proceedings of the American Mathematical Society, 7(1), 48–50.CrossRefGoogle Scholar
  2. Massimo, B., & Sacco, P. L. (2010). Auto-contractive maps, the H function, and the maximally regular graph (MRG): a new methodology for data mining (chapter 11). In V. Capecchi et al. (Eds.), Applications of mathematics in models, artificial neural networks and arts. Dordrecht/London: Springer. doi: 10.1007/978-90-481-8581-8_11.Google Scholar

Software

  1. Buscema, M. (2007). Contractive Maps. Software for programming Auto Contractive Maps (Semeion Software #15, v. 2), Rome.Google Scholar
  2. Buscema, M. (2007). Constraints Satisfaction Networks. Software for programming Non Linear Auto-Associative Networks, Semeion Software #14 (v. 10), Rome.Google Scholar
  3. Buscema, M. (2008). MST. Software for programming Trees from artificial networks weights matrix (Semeion Software #38, v 5), Rome.Google Scholar
  4. Massini, G. (2007). Tree Visualizer. Software to draw and manipulate tree graph (Semeion Software #40, v. 3), Rome.Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.Semeion Research Center of Sciences of CommuicationRomeItaly

Personalised recommendations