Social Data Analysis by Non-Linear Imbedding

Abstract

Political science datasets contain information of interest to planners seeking to predict international relations. The goal of this project was to use modern data mining techniques to determine whether such data exists and, if so, to characterize it. We developed a new approach for such analysis based on geometric harmonics. At the heart of our approach is the observation that such relationships are inherently nonlinear and that the data are noisy and incomplete. To demonstrate the power and usefulness of our techniques the focus was on United Nations voting data. It was shown that major historical events could be inferred from these data; that other (linear) techniques did not suffice; and that they could be extended to understanding certain aspects of international relations. We conclude that the project was successful in opening up the field of computational international relations.

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Document Details

Document Type
Technical Report
Publication Date
Sep 20, 2013
Accession Number
AD1013125

Entities

People

  • Steven W. Zucker

Organizations

  • Yale University

Tags

Communities of Interest

  • C4I
  • Energy and Power Technologies
  • Weapons Technologies

DTIC Thesaurus Topics

  • Abstracts
  • Applied Mathematics
  • Data Analysis
  • Data Mining
  • Electronic Mail
  • European Communities
  • Factor Analysis
  • Information Processing
  • Intergovernmental Organizations
  • International Relations
  • Political Science
  • Public Policy
  • Security
  • Social Sciences
  • Three Dimensional
  • Two Dimensional
  • Ussr

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Calculus or Mathematical Analysis
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference