Data Mining Methods for Terrorist Activity

Abstract

Identification of outliers is an important component of data mining since such observations can have a profound influence and distort the analysis. In the recent years, detection of outlier has been broadly applied in the field of data mining. It is an important method for many research applications such as credit card fraud, computer intrusion detection, and criminal activities in electronic commerce, medical diagnosis and anti-terrorism, image processing. Detection of outliers-objects which is abnormal away from the remaining of the data set. In this paper, to modify a definition of neighbourhood parameter in information system has been proposed to discover the outliers in rough set theory. The computational results are compared with other existing methods.

Keywords: Data mining, rough set, value difference metric and neighborhood outlier

Cite this Article

Ezhilarasi R, P. Arumugam. Data Mining Methods for Terrorist Activity. Research & Reviews Journal of Statistics. 2019; 8(1): 38–43p.