Abstract
Both fuzzy set theory and rough set theory play an important role in data-driven, systems modelling and analysis. They have been successfully applied to building various intelligent decision support systems (amongst many others). This paper presents an integrated utilisation of some recent advances in these theories for detection and prevention of serious crime (e.g. terrorism). It is shown that the use of these advanced theories offers an effective means for the generation and assessment of plausible scenarios which can each provide an explanation for the given intelligence data. The resulting systems have the potential to facilitate rapid response in devising and deploying preventive measures. The paper also suggests a number of important further challenges in consolidating and refining such systems.
This work was supported by UK EPSRC grants GR/S63267/01-02, GR/S98603/01 and EP/D057086/1. The author is grateful to all members of the project teams for their contributions, but will take full responsibility for the views expressed here.
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Shen, Q. (2009). Fuzzy Sets and Rough Sets for Scenario Modelling and Analysis. In: Sakai, H., Chakraborty, M.K., Hassanien, A.E., Ślęzak, D., Zhu, W. (eds) Rough Sets, Fuzzy Sets, Data Mining and Granular Computing. RSFDGrC 2009. Lecture Notes in Computer Science(), vol 5908. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10646-0_5
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DOI: https://doi.org/10.1007/978-3-642-10646-0_5
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