Applying unsupervised machine learning to counterterrorism
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Transportation
Link
https://link.springer.com/content/pdf/10.1007/s42001-022-00164-w.pdf
Reference49 articles.
1. Abrahms, M., & Conrad, J. (2017). The strategic logic of credit claiming: A new theory for anonymous terrorist attacks. Security Studies, 26(2), 279–304. https://doi.org/10.1080/09636412.2017.1280304
2. Adnan, M., & Rafi, M. (2015). Extracting patterns from global terrorist dataset (GTD) using co-clustering approach. Journal of Independent Studies and Research, 13(1), 7. https://doi.org/10.31645/jisrc/(2015).13.1.0002
3. Aggarwal, C. C. (2015). Data mining. Springer International Publishing.
4. Agresti, A. (2018). Statistical methods for the social sciences (5th ed.). Pearson.
5. Aleroud, A., & Gangopadhyay, A. (2018). Multimode co-clustering for analyzing terrorist networks. Information Systems Frontiers, 20(5), 1053–1074. https://doi.org/10.1007/s10796-016-9712-4
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