Abstract:

This paper presents a pattern recognition approach in multidimensional databases. The approach is based on a clustering method using the distance measurement between a reference profile and other database observations. Two distance measurements will be proposed: an adaptation of the Khi2 formula to the multidimensional context, extracted from the Multiple Correspondence Analysis (MCA), and the Euclidean distance. A comparison between the two distances will be provided to retain the most pertinent one for the multidimensional clustering context. Our approach will then be applied to a real case study dealing with armed attacks worldwide stored in the Global Terrorism Database (GTD) .