Clustering heterogeneous semi-structured social science datasets

D. B. Skillicorn, C. Leuprecht

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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, details of every terrorist attack since 1970; a Chicago police dataset, details of every drug-related incident over a period of approximately a month; and a dataset describing members of a Hezbollah crime/terror network within the U.S.
Original languageEnglish
Pages (from-to)2908-2912
Number of pages5
JournalProcedia Computer Science
Publication statusPublished - 01 Jan 2015


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