Towards Mining of Stakeholders in Criminal Organizations from Telecommunication Metadata: Analytic Approach to Latent Feature Extraction

Abideen A. Ismail, E. N. Onwuka, B. A. Salihu, C. O. Ubadike

Abstract


Mining techniques extract implicit, previously unknown and potentially useful information from data towards providing a solution to future challenges. Mining involves analysis of links in existing datasets. For fighting crime, its efficiency and robustness are on active covert criminals and not on silent key-players like criminal stakeholders (CS). The analytic approach attempts to differentiate a CS from well-known covert nodes in a social network structure (SNS). A CS is a two-face-attribute covert node; a bottle-neck for mining techniques. Some conceptually general attributes often used to describe covert nodes are examined and analyzed to figure out a unique latent feature for a CS. It is found that forceful use of influence and vulnerability are bogus for description and detection of a CS. Our approach arrived at a new feature for the description and identification of a CS. The new feature is dynamic and it is not self-sufficient like vulnerability and influence that is general to the covert nodes, or network leader (NL). Discovered attribute for mining CSs depends on NL’s position in the structure.

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