Extracting patterns from Global Terrorist Dataset (GTD) Using Co-Clustering approach

Abstract

Global Terrorist Dataset (GTD) is a vast collection of terrorist activities reported around the globe. The terrorism database incorporates more than 27,000 terrorism incidents from 1968 to 2014. Every record has spatial data, a period stamp, and a few different fields (e.g. strategies, weapon sorts, targets and wounds). There were few earlier studies to find interesting patterns from this textual gamut of data. The author believes that GTD has numerous interesting patterns still hidden and the full potential of this resource is still to be divulged. In this Independent Study, the author tries to investigate the GTD through co-clustering method for pattern discovery. Author has extracted textual data from GTD as per motivation to cluster the data in space and time simultaneously, through co-clustering. Co-clustering has become an important and powerful tool for data mining. By using co-clustering, bilateral data can be analysed by describing the connections between two different entities. There are many applications in the real world that can extensively benefits from this approach of co-clustering, such as market basket analysis and recommendation system. In this study, the effectiveness of coclustering model will be described by performing experiment on database of global terrorist events.