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 the 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 efficient one for the multidimensional clustering context. The proposed approach will be applied to a real case study representing armed attacks worldwide stored in the Global Terrorism Database (GTD).

Semeh BEN SALEM and Sami NAOUALI, “Pattern Recognition Approach in Multidimensional Databases: Application to the Global Terrorism Database” International Journal of Advanced Computer Science and Applications(IJACSA), 7(8), 2016. http://dx.doi.org/10.14569/IJACSA.2016.070838

@article{SALEM2016,
title = {Pattern Recognition Approach in Multidimensional Databases: Application to the Global Terrorism Database},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2016.070838},
url = {http://dx.doi.org/10.14569/IJACSA.2016.070838},
year = {2016},
publisher = {The Science and Information Organization},
volume = {7},
number = {8},
author = {Semeh BEN SALEM and Sami NAOUALI}
}