Clustering Heterogeneous Semi-structured Social Science Datasets for Security Applications

D. B. Skillicorn, C. Leuprecht

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

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.

Original languageEnglish
Title of host publicationAdvanced Sciences and Technologies for Security Applications
EditorsAnthony J. Masys
Place of PublicationCham, Switzerland
PublisherSpringer
Pages181-191
Number of pages11
ISBN (Electronic)978-3-319-78021-4
ISBN (Print)978-3-319-78020-7
DOIs
Publication statusPublished - 2018

Publication series

NameAdvanced Sciences and Technologies for Security Applications
ISSN (Print)1613-5113
ISSN (Electronic)2363-9466

Keywords

  • Chicago policing
  • Clustering
  • Crime
  • Global terrorism database
  • Hashing
  • Hezbollah
  • Terrorism

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