Australian Institute of Criminology

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Using data mining techniques to detect criminal networks

Australian Institute of Criminology, John Zeleznikow
06 April 2005

Professor John Zeleznikow
School of Information Systems, Victoria University, Melbourne

Seminar held
6 April 2005
Australian Institute of Criminology

Introduction

Professor Zeleznikow presented a discussion of data mining and decision support technologies for police and crime investigators. The discussion was very practical, with examples taken from work undertaken with three United Kingdom police forces.

The lessons learned were presented, along with their relevance to future work. Significant aspects of the knowledge discovery from databases process were described, starting with an examination of the data that police collect and the reasons for storing such data, and progressing to the development of crime matching and predictive knowledge that is operationalised in decision support software.

Professor Zeleznikow is a past director of the Joseph Bell Centre for Forensic Statistics and Legal Reasoning at the University of Edinburgh Law School and Director of the Donald Berman Laboratory for Information Technology and Law at La Trobe University. He has co-authored Building intelligent legal information systems: knowledge representation and reasoning in law, and Knowledge discovery from legal databases, and numerous articles and chapters in books.

Related references

  • COPLINK: About | Evaluation
  • Decision support systems for police : lessons from the application of data mining techniques to 'soft' forensic evidence (PDF 1.18MB)
    G C Oatley, B W Ewart and J Zeleznikow, 2004
  • Matching and predicting crimes (PDF 90kB)
    G C Oatley, J Zeleznikow and B W Ewart, 2004. In A Macintosh, R Ellis, and T Allen (eds.), Applications and innovations in intelligent systems XII. Proceedings of AI2004 The twenty-fourth SGAI international conference on knowledge based systems and applications of artificial intelligence, Springer: 19-32
  • A hybrid rule - neural approach for the automation of legal reasoning in the discretionary domain of family law in Australia (PDF 310kB)
    Andrew Stranieri, John Zeleznikow, Mark Gawler and Bryn Lewis, 1999. Artificial Intelligence and Law 7(2-3):153-183