Exploring the Historical Behavior of Islamic State Groups: Latent Class Analysis and K-Modes Clustering Approach

dc.contributor.advisor Jaramillo Villegas, José Alfredo
dc.contributor.author Varona Henao, Daniel
dc.contributor.author Toquica Arango, Mateo
dc.date.accessioned 2022-05-19T14:09:57Z
dc.date.available 2022-05-19T14:09:57Z
dc.date.issued 2021
dc.description.abstract Research on terrorism has always gravitated around qualitative methods and statistical techniques. Technology plays an essential role in terrorism and counterterrorism analysis by providing collections of large databases in many fields and the computational power to analyze them. Machine learning has shown new methods that could complement standard and well-established methodological approaches. This work contributes to the bridging of machine learning with terrorism studies by analyzing data with a classic statistical method Latent Class Analysis (LCA), and a machine learning method (K-modes). More formally, this work presents a mixed approach to analyze and cluster records from the Global Terrorism Database (GTD) referring to terrorist attacks belonging to the Islamic State. A diverse set of variables are considered, such as the type of weapons, targets, terrorist groups perpetrating the attacks, and geographic location. We identified three analysis periods by relying on a literature review and applied and contrasted LCA and K-Mode models for each period. This project aims to generate a record of how the periods were divided and identify the critical points for using the variables in the GTD database. Finally, we performed a data classification and generated an analysis for whoever requires it for these terrorist groups in the established periods. eng
dc.description.abstract Este trabajo presenta un enfoque mixto para analizar y agrupar registros referentes a ataques terroristas pertenecientes al Estado Islámico, utilizando datos estructurados de la Global Terrorism Database (GTD) agrupados por ataques terroristas en función de diferentes variables a analizar, como el tipo de armas, objetivos que persigue el ataque, nombre del grupo terrorista, ubicación geográfica, entre otros; Para el posterior análisis de la información obtenida a través de artículos científicos que sustenten los hechos. A través de Kmodes se analizó la información obtenida, y se separó en grupos por características específicas y por bloques de años que permitieron analizar hechos históricos relevantes para la humanidad; Así, a través de los artículos mencionados anteriormente, se identificó la relación entre los datos ubicados en la base de datos GTD y los hechos. spa
dc.description.degreelevel Maestría
dc.description.degreename Magíster en Ingeniería de Sistemas y Computación
dc.description.tableofcontents Contents 1 Introduction 7 1.1 Problem Description . . . . . . . . . . . . . . . . . . . . . . . . . 7 1.2 General and Specific Objectives . . . . . . . . . . . . . . . . . . . 8 1.2.1 General Objective . . . . . . . . . . . . . . . . . . . . . . . 8 1.2.2 Specific Objectives . . . . . . . . . . . . . . . . . . . . . . 8 1.3 Background and Justification . . . . . . . . . . . . . . . . . . . . 9 1.4 Viability and scope . . . . . . . . . . . . . . . . . . . . . . . . . . 10 1.5 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 1.5.1 Hypothesis . . . . . . . . . . . . . . . . . . . . . . . . . . 10 1.5.2 Methodological Design . . . . . . . . . . . . . . . . . . . . 10 1.5.2.1 Data Mining . . . . . . . . . . . . . . . . . . . . 11 1.5.2.2 Identify Criteria . . . . . . . . . . . . . . . . . . 12 1.5.2.3 Period Division . . . . . . . . . . . . . . . . . . . 12 1.5.3 Optimal Clusters . . . . . . . . . . . . . . . . . . . . . . . 12 1.5.4 Implemented Models . . . . . . . . . . . . . . . . . . . . . 13 1.5.4.1 Latent Class Analysis . . . . . . . . . . . . . . . . 13 3 1.5.4.2 K-Modes . . . . . . . . . . . . . . . . . . . . . . 15 1.5.4.3 Model Comparison . . . . . . . . . . . . . . . . . 16 1.6 Project sustainability . . . . . . . . . . . . . . . . . . . . . . . . . 18 1.7 Administrative Aspects . . . . . . . . . . . . . . . . . . . . . . . . 18 1.7.1 Necessary Resources: Physical, Logistic, and Human. . . . 18 1.7.2 Sources of Funding . . . . . . . . . . . . . . . . . . . . . . 19 1.7.3 Schedule . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 2 State of the Art Review 21 2.1 Terrorism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 2.2 Studies Where LCA and K-Modes are Applied . . . . . . . . . . . 27 2.2.1 LCA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 2.2.2 K-Modes . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 3 Theorethical Framework 32 3.1 Terrorism Study Techniques . . . . . . . . . . . . . . . . . . . . . 32 3.2 Terrorism Issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 3.3 Islamic Terrorism . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 4 Thesis Development 41 4.1 Global Terrorism Database . . . . . . . . . . . . . . . . . . . . . . 41 4.2 Period Division . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 4.2.1 Period 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 4.2.2 Period 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 4.2.3 Period 3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 4.3 Data Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 4 4.4 Exploratory data analysis . . . . . . . . . . . . . . . . . . . . . . 60 4.4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 60 4.4.2 Lashkar-e-Islam . . . . . . . . . . . . . . . . . . . . . . . . 60 4.4.3 Al-Qaida in the Islamic Maghreb (AQIM) . . . . . . . . . . 62 4.4.4 Al-Qaida . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 4.4.5 Jemaah Islamiyah . . . . . . . . . . . . . . . . . . . . . . 67 4.5 Latent Class Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 70 4.5.1 Data Input . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 4.5.2 Command Options . . . . . . . . . . . . . . . . . . . . . . 70 4.6 K-Modes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 4.6.1 Input . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 4.6.2 Deploy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 4.7 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 4.7.1 Period 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 4.7.2 Period 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86 5 Conclusions and Future Works 99 5.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 5.1.1 Data processing . . . . . . . . . . . . . . . . . . . . . . . . 99 5.1.2 Third period . . . . . . . . . . . . . . . . . . . . . . . . . 100 5.1.3 Groups evolution . . . . . . . . . . . . . . . . . . . . . . . 100 5.1.4 LCA and K-Modes . . . . . . . . . . . . . . . . . . . . . . . 101 5.1.5 Method comparison . . . . . . . . . . . . . . . . . . . . . 102 5.1.5.1 LCA List . . . . . . . . . . . . . . . . . . . . . . . 102 5.1.5.2 K-Modes list . . . . . . . . . . . . . . . . . . . . 103 eng
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dc.identifier.instname Universidad Tecnológica de Pereira
dc.identifier.reponame Repositorio institucional Universidad Tecnológica de Pereira
dc.identifier.repourl https://repositorio.utp.edu.co/home
dc.identifier.uri https://hdl.handle.net/11059/14112
dc.language.iso eng
dc.publisher Universidad Tecnológica de Pereira
dc.publisher.faculty Facultad de Ingenierías
dc.publisher.place Pereira
dc.publisher.program Maestría en Ingeniería de Sistemas y Computación
dc.relation.references O. E. Dictionary. ”terrorism, n.”., tipo @ONLINE. [Online]. Available: https://www.oed.com/view/Entry/199608?redirectedFrom=Terrorism
dc.relation.references L. G. Miller E, Dugan L. ”university of maryland, n.”., tipo @ONLINE. [Online]. Available: https://start.umd.edu/gtd/
dc.relation.references C. A. Hannah Ritchie, Joe Hasell and M. Roser, “Terrorism,” Our World in Data, 2013, https://ourworldindata.org/terrorism.
dc.relation.references A. S. Alsaedi, A. S. Almobarak, and S. T. Alharbi, “Mining the global ter rorism dataset using machine learning algorithms,” in 2019 IEEE/ACS 16th International Conference on Computer Systems and Applications (AICCSA). IEEE, 2019, pp. 1–7
dc.relation.references B. K. Smith, M. Stohl, and M. Al-Gharbi, “Discourses on countering vi olent extremism: the strategic interplay between fear and security after 9/11,” Critical Studies on Terrorism, vol. 12, no. 1, pp. 151–168, 2019.
dc.relation.references R. Kitchin and N. Thrift, International encyclopedia of human geography. Elsevier, 2009.
dc.relation.references S. Sloan, “Technology and terrorism: Privatizing public violence,” IEEE Technology and Society Magazine, vol. 10, no. 2, pp. 8–14, 1991.
dc.relation.references S. Nie and D. Sun, “Research on counter-terrorism based on big data,” in 2016 IEEE International Conference on Big Data Analysis (ICBDA). IEEE, 2016, pp. 1–5.
dc.relation.references R. E. Berkebile, “What is domestic terrorism? a method for classifying events from the global terrorism database,” Terrorism and political vio lence, vol. 29, no. 1, pp. 1–26, 2017
dc.relation.references C. Santos, T. El Zahran, J. Weiland, M. Anwar, and J. Schier, “Charac terizing chemical terrorism incidents collected by the glob database, 1970–2015,” Prehospital and disaster medicine, vol. 34, no. 4, p. 385, 2019.
dc.relation.references Y. Woo-suk, “Periodical and spatial differences of terrorism examining global terrorism database from 1970˜ 2018.”
dc.relation.references J. Han, J. Pei, and M. Kamber, Data mining: concepts and techniques. Elsevier, 2011.
dc.relation.references C. C. Aggarwal and P. S. Yu, “Data mining techniques for associations, clustering and classification,” in Methodologies for Knowledge Discovery and Data Mining, N. Zhong and L. Zhou, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999, pp. 13–23
dc.relation.references D. Talreja, J. Nagaraj, N. Varsha, and K. Mahesh, “Terrorism analytics: Learning to predict the perpetrator,” in 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE, 2017, pp. 1723–1726.
dc.relation.references A. Keivan Hosseiny, “The evolution of european anti-americanism in the post-september era,” POLITICAL QUARTERLY, vol. 51, no. 2, pp. 563– 537, 2021.
dc.relation.references A. K. Hosseiny, “The evolution of european anti-americanism in the post september 11 era.”
dc.relation.references D. A. Linzer, J. B. Lewis et al., “polca: An r package for polytomous vari able latent class analysis,” Journal of statistical software, vol. 42, no. 10, pp. 1–29, 2011.
dc.relation.references T. Hastie, R. Tibshirani, and J. Friedman, The elements of statistical learn ing: data mining, inference, and prediction. Springer Science & Business Media, 2017.
dc.relation.references R. Sathya and A. Abraham, “Comparison of supervised and unsupervised learning algorithms for pattern classification,” International Journal of Advanced Research in Artificial Intelligence, vol. 2, no. 2, pp. 34–38, 2013
dc.relation.references C. R. C. Lopez, A. M. Rivas, and L. O. Zarate, “Modelos de clases la- ´ tentes para definir perfiles conductuales en ninos de 4 y 5 a ˜ nos.” ˜ Revista Electronica de Psicolog ´ ´ıa Iztacala, vol. 14, no. 1, p. 354, 2011
dc.relation.references N. Denson and M. Ing, “Latent class analysis in higher education: An illustrative example of pluralistic orientation,” Research in Higher Educa tion, vol. 55, no. 5, pp. 508–526, 2014.
dc.relation.references S. Graf and M. Cecchini, “Identifying patterns of unhealthy diet and phys ical activity in four countries of the americas: a latent class analysis,” Revista Panamericana de Salud Publica ´ , vol. 42, p. e56, 2018.
dc.relation.references Z. Huang and M. K. Ng, “A fuzzy k-modes algorithm for clustering cate gorical data,” IEEE transactions on Fuzzy Systems, vol. 7, no. 4, pp. 446– 452, 1999
dc.relation.references F. Cao, J. Liang, D. Li, L. Bai, and C. Dang, “A dissimilarity measure for the k-Modes clustering algorithm,” Knowledge-Based Systems, vol. 26, pp. 120–127, 2012. [Online]. Available: http: //dx.doi.org/10.1016/j.knosys.2011.07.011
dc.relation.references Y. B. G. Challenger P´erez, Ivet D´ıaz Ricardo and R. Antonio, “El lenguaje de programacion python,” ´ Ciencias Holgu´ın, 2014. [Online]. Available: https://www.redalyc.org/articulo.oa?id=181531232001
dc.relation.references N. J. de Vos, “kmodes categorical clustering library,” https://github.com/ nicodv/kmodes, 2015-2021.
dc.relation.references Z. Huang, “Extensions to the k-means algorithm for clustering large data sets with categorical values,” Data mining and knowledge discovery, vol. 2, no. 3, pp. 283–304, 1998.
dc.relation.references ——, “Clustering large data sets with mixed numeric and categorical values,” in In The First Pacific-Asia Conference on Knowledge Discovery and Data Mining, 1997, pp. 21–34.
dc.relation.references F. Cao, J. Liang, and L. Bai, “A new initialization method for categorical data clustering,” Expert Systems with Applications, vol. 36, no. 7, pp. 10 223–10 228, 2009. [Online]. Available: https: //www.sciencedirect.com/science/article/pii/S0957417409001043
dc.relation.references J. Barker, El sinsentido del terrorismo. Intermon Oxfam Editorial, 2004, ´ vol. 22
dc.relation.references F. C. Navarro Cardoso, “Criptomonedas (en especial, bitcoin) y blanqueo ´ de dinero,” Revista electronica de ciencia penal y criminolog ´ ´ıa, 2019.
dc.relation.references A. P. Schmid, J. J. Forest, and T. Lowe, “Terrorism studies,” Perspectives on Terrorism, vol. 15, no. 3, pp. 142–152, 2021
dc.relation.references A. Andary, “Terrorism and counter-terrorism,” vol. 1, p. 14, 01 2021.
dc.relation.references T. G. R. Morales, “El terrorismo y nuevas formas de terrorismo,” Espacios Publicos ´ , vol. 15, no. 33, pp. 72–95, 2012.
dc.relation.references P. H. Pilley and S. Sikchi, “Model for predicting terrorist group by clope algorithm,” 2014.
dc.relation.references D. Holbrook and J. Horgan, “Terrorism and ideology,” Perspectives on Ter rorism, vol. 13, no. 6, pp. 2–15, 2019.
dc.relation.references S. G. Jones, C. Doxsee, and N. Harrington, “The escalating terrorism problem in the united states,” 2020
dc.relation.references D. Meierrieks and F. Schneider, “Terrorism and international economic policy,” European Journal of Political Economy, vol. 69, p. 102011, 2021. [Online]. Available: https://www.sciencedirect.com/science/article/pii/ S0176268021000124
dc.relation.references C. Seabra, P. Reis, and J. L. Abrantes, “The influence of terrorism in tourism arrivals: A longitudinal approach in a mediterranean country,” Annals of Tourism Research, vol. 80, p. 102811, 2020.
dc.relation.references L. de la Calle and I. Sanchez-Cuenca, “Rebels without a Territory,” ´ Journal of Conflict Resolution, vol. 56, no. 4, pp. 580–603, aug 2012. [Online]. Available: http://journals.sagepub.com/doi/10.1177/ 0022002711431800
dc.relation.references D. M. D. Silva, “The Othering of Muslims: Discourses of Radicalization in the New York Times , 1969-2014,” Sociological Forum, vol. 32, no. 1, pp. 138–161, mar 2017. [Online]. Available: http://doi.wiley.com/10. 1111/socf.12321
dc.relation.references P. J. Schraeder and M. J. Schumacher, “Collective Action, Foreign Fighting, and the Global Struggle for the Islamic State,” Democracy and Security, vol. 16, no. 3, pp. 234–259, jul 2020. [Online]. Available: https: //www.tandfonline.com/doi/full/10.1080/17419166.2020.1802690
dc.relation.references J. Baudrillard, The spirit of terrorism and other essays. Verso Trade, 2013.
dc.relation.references N. Saiya, “Blasphemy and terrorism in the Muslim world,” Terrorism and Political Violence, vol. 29, no. 6, pp. 1087–1105, nov 2017. [Online]. Available: https://www.tandfonline.com/doi/full/10.1080/09546553. 2015.1115759
dc.relation.references K. A. Powell, “Framing Islam/Creating Fear: An Analysis of US Media Coverage of Terrorism from 2011-2016,” RELIGIONS, vol. 9, no. 9, sep 2018.
dc.relation.references M. Dynel and F. I. M. Poppi, “In tragoedia risus: Analysis of dark hu mour in post-terrorist attack discourse,” DISCOURSE & COMMUNICA TION, vol. 12, no. 4, pp. 382–400, 2018.
dc.relation.references B. K. Smith, M. Stohl, and M. Al-Gharbi, “Discourses on countering violent extremism: the strategic interplay between fear and security after 9/11,” Critical Studies on Terrorism, vol. 12, no. 1, pp. 151–168, jan 2019. [Online]. Available: https://www.tandfonline.com/doi/full/ 10.1080/17539153.2018.1494793
dc.relation.references J. Hargreaves, “Police stop and search within british muslim communi ties: Evidence from the crime survey 2006–11,” The British Journal of Criminology, vol. 58, no. 6, pp. 1281–1302, 2018
dc.relation.references D. O. P´erez and J. M. A. Izquierdo, “Analisis de clases latentes como ´ t´ecnica de identificacion de tipolog ´ ´ıas,” Revista INFAD de Psicología. International Journal of Developmental and Educational Psychology., vol. 5, no. 1, pp. 251–260, 2019.
dc.rights Manifiesto (Manifestamos) en este documento la voluntad de autorizar a la Biblioteca Jorge Roa Martínez de la Universidad Tecnológica de Pereira la publicación en el Repositorio institucional (http://biblioteca.utp.edu.co), la versión electrónica de la OBRA titulada: ________________________________________________________________________________________________ ________________________________________________________________________________________________ ________________________________________________________________________________________________ La Universidad Tecnológica de Pereira, entidad académica sin ánimo de lucro, queda por lo tanto facultada para ejercer plenamente la autorización anteriormente descrita en su actividad ordinaria de investigación, docencia y publicación. La autorización otorgada se ajusta a lo que establece la Ley 23 de 1982. Con todo, en mi (nuestra) condición de autor (es) me (nos) reservo (reservamos) los derechos morales de la OBRA antes citada con arreglo al artículo 30 de spa
dc.rights.accessrights info:eu-repo/semantics/openAccess
dc.rights.coar http://purl.org/coar/access_right/c_abf2
dc.rights.license Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject.ddc 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores
dc.subject.other Clustering methods
dc.subject.other Digital simulation
dc.subject.other Digital systems
dc.subject.proposal Clustering eng
dc.subject.proposal K- Models eng
dc.subject.proposal Lantent class analysis eng
dc.title Exploring the Historical Behavior of Islamic State Groups: Latent Class Analysis and K-Modes Clustering Approach eng
dc.type Trabajo de grado - Maestría
dc.type.coar http://purl.org/coar/resource_type/c_bdcc
dc.type.coarversion http://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.content Text
dc.type.driver info:eu-repo/semantics/masterThesis
dc.type.version info:eu-repo/semantics/acceptedVersion
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