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Clustering Heterogeneous Semi-structured Social Science Datasets for Security Applications

  • D. B. Skillicorn
  • C. LeuprechtEmail author
Chapter
Part of the Advanced Sciences and Technologies for Security Applications book series (ASTSA)

Abstract

Social scientists have begun to collect large datasets that are heterogeneous and semi-structured, but the ability to analyze such data has lagged behind its collection. We design a process to map such datasets to a numerical form, apply singular value decomposition clustering, and explore the impact of individual attributes or fields by overlaying visualizations of the clusters. This provides a new path for understanding such datasets, which we illustrate with three real-world examples: the Global Terrorism Database, which records details of every terrorist attack since 1970; a Chicago police dataset, which records details of every drug-related incident over a period of approximately a month; and a dataset describing members of a Hezbollah crime/terror network in the U.S.

Keywords

Clustering Hashing Terrorism Crime Global terrorism database Chicago policing Hezbollah 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of ComputingQueen’s UniversityKingstonCanada
  2. 2.Political ScienceRoyal Military College of CanadaKingstonCanada
  3. 3.Flinders University of South AustraliaAdelaideAustralia

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