Integrative Data Mining for Assessing International Conflict Events

Abstract

State failure has been traditionally defined as the collapse of national authority, which may be reflected in disasters such as wars and disruptive regime transitions. The availability of comprehensive datasets and the limitations exhibited by previous forecasting analyses led us to integrate different predictive resources and models through statistical analysis and machine learning. Here we demonstrate the predictive ability of unsupervised and supervised learning approaches to detecting meaningful relationships between country cases, encoded by several socio-economic indicators, and the emergence of violent conflicts. Two clustering-based analyses (Kohonen maps and a network-based approach) provided the basis for exploratory analyses that confirmed hypotheses about the relevance of the data and the differences between state failure types. We also illustrate the potential of a novel network-based clustering approach for sub-class discovery in the area of political instability analysis. Furthermore, we show significant relationships between the emergence of violent conflicts and a dataset of quantitative indicators of good governance, which allows the design of effective supervised and unsupervised classifiers. This study contributes to the development of intelligent data analysis techniques for supporting hypothesis generation and testing in international conflict analyses