Enhancing Financial Fraud Detection using Temporal Patter Mining Technique

Ahmed Aziz^1,*, Sanjar Mirzaliev¹

¹Tashkent State University of Economics, Tashkent, Uzbekistan

Emails: a.mohamed@tsue.uz; sanjar2611@gmail.com

Abstract

Examining the temporal behavior of common patterns, obtaining appropriate clusters, and reducing the size of discovered patterns are three significant challenges in temporal data mining. Among the available methods, the constraint-based pattern mining approach has achieved remarkable progress in this domain. Apriori and Interleaved algorithms, which are both slow and outdated, are nonetheless used by present time-granularity pattern exploration approaches. To address these issues, we propose the Frequent Pattern Growth method with Special Constraints. The system incorporates a method for generating patterns on a regular basis. It mandates that transactional datasets adhere to complete and partial cyclic criteria. To locate all possible periodic patterns within the Spatio temporal database, we redefine the task as periodic pattern mining in this thesis. The proposed method makes use of a periodic pattern tree miner. To begin, the clustering method uses an innovative global pollination artificial fish swarm technique to create the most effective dense clusters.

Keywords: Fraud Detection; Financial Fraud Detection; Temporal Patter Mining Technique; Naïve Bayes; Radial Bias Neural Network