Abstract
This paper describes some possible uses of Formal Concept Analysis in the detection and monitoring of Organised Crime. After describing FCA and its mathematical basis, the paper suggests, with some simple examples, ways in which FCA and some of its related disciplines can be applied to this problem domain. In particular, the paper proposes FCA-based approaches for finding multiple instances of an activity associated with Organised Crime, finding dependencies between Organised Crime attributes, and finding new indicators of Organised Crime from the analysis of existing data. The paper concludes by suggesting that these approaches will culminate in the creation and implementation of an Organised Crime ‘threat score card’, as part of an overall environmental scanning system that is being developed by the new European ePOOLICE project.
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References
Andrews, S.: In-close2, a high performance formal concept miner. In: Andrews, S., Polovina, S., Hill, R., Akhgar, B. (eds.) ICCS 2011. LNCS, vol. 6828, pp. 50–62. Springer, Heidelberg (2011)
Andrews, S., Orphanides, C.: FcaBedrock, a formal context creator. In: Croitoru, M., Ferré, S., Lukose, D. (eds.) ICCS 2010. LNCS, vol. 6208, pp. 181–184. Springer, Heidelberg (2010)
Andrews, S., Orphanides, C.: Knowledge discovery through creating formal contexts, pp. 455–460. IEEE Computer Society (2010)
Becker, P., Correia, J.H.: The ToscanaJ Suite for Implementing Conceptual Information Systems. In: Ganter, B., Stumme, G., Wille, R. (eds.) Formal Concept Analysis. LNCS (LNAI), vol. 3626, pp. 324–348. Springer, Heidelberg (2005)
Europol. Eu organised crime threat assessment: Octa 2011. file no. 2530-274. Technical report, Europol, O2 Analysis & Knowledge, The Hague (2011)
Frank, A., Asuncion, A.: UCI machine learning repository (2010), http://archive.ics.uci.edu/ml
Ganter, B., Kuzntesov, S.O.: Formalizing hypotheses with concepts. In: Ganter, B., Mineau, G.W. (eds.) ICCS 2000. LNCS (LNAI), vol. 1867, pp. 342–356. Springer, Heidelberg (2000)
Ganter, B., Wille, R.: Formal Concept Analysis: Mathematical Foundations. Springer (1998)
Goethals, B.: Frequent itemset mining implementations repository, http://fimi.ua.ac.be/
Goethals, B., Zaki, M.: Advances in frequent itemset mining implementations: Report on fimi’03. SIGKDD Explorations Newsletter 6(1), 109–117 (2004)
Imberman, S., Domanski, D.: Finding association rules from quantitative data using data booleanization (1999)
Kuznetsov, S.O.: Mathematical aspects of concept analysis. Journal of Mathematical Science 18, 1654–1698 (1996)
Outrata, J., Vychodil, V.: Fast algorithm for computing fixpoints of galois connections induced by object-attribute relational data. Inf. Sci. 185(1), 114–127 (2012), doi:10.1016/j.ins.2011.09.023
Pastor, R.: epoolice: Early pusuit against organised crime using environmental scanning, the law and intelligence systems (2013), https://www.epoolice.eu/
Priss, U.: Formal concept analysis in information science. Annual Review of Information Science and Technology (ASIST) 40 (2008)
United Nations: Global programme against transnational organized crime. Results of a pilot survey of forty selected organized criminal groups in sixteen countries. Technical report, United Nations: Offcie on Drugs and Crime (2002)
General Secretariat. Serious and organised crime threat assessment (socta) - methodology. Technical report, Council of the European Union (2012)
CISC Strategic Criminal Analytical Services. Strategic early warning for criminal intelligence. Technical report, Criminal Intelligence Service Canada (CISC), Central Bureau, Ottawa (2007)
Valtchev, P., Missaoui, R., Godin, R.: Formal concept analysis for knowledge discovery and data mining: The new challenges. In: Eklund, P. (ed.) ICFCA 2004. LNCS (LNAI), vol. 2961, pp. 352–371. Springer, Heidelberg (2004)
Yevtushenko, S.A.: System of data analysis “concept explorer”. In: Proceedings of the 7th National Conference on Artificial Intelligence KII 2000, pp. 127–134 (2000) (in Russian)
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Andrews, S., Akhgar, B., Yates, S., Stedmon, A., Hirsch, L. (2013). Using Formal Concept Analysis to Detect and Monitor Organised Crime. In: Larsen, H.L., Martin-Bautista, M.J., Vila, M.A., Andreasen, T., Christiansen, H. (eds) Flexible Query Answering Systems. FQAS 2013. Lecture Notes in Computer Science(), vol 8132. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40769-7_11
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DOI: https://doi.org/10.1007/978-3-642-40769-7_11
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