Clustering heterogeneous semi-structured social science datasets for security applications

D. B. Skillicorn, C. Leuprecht

Research output: Book chapter/Published conference paperChapter (peer-reviewed)peer-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 publicationSecurity by Design
Subtitle of host publicationInnovative Perspectives on Complex Problems
EditorsAnthony J. Masys
PublisherSpringer
Chapter9
Pages181-191
Number of pages11
Edition1st
ISBN (Electronic)9783319780214
ISBN (Print)9783319780207
DOIs
Publication statusPublished - 2018

Publication series

NameAdvanced Sciences and Technologies for Security Applications

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