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The Scientific World Journal
Volume 2014 (2014), Article ID 252797, 10 pages
http://dx.doi.org/10.1155/2014/252797
Research Article

FraudMiner: A Novel Credit Card Fraud Detection Model Based on Frequent Itemset Mining

Department of Computer Science, Jamia Hamdard University, New Delhi 110062, India

Received 30 April 2014; Revised 14 August 2014; Accepted 24 August 2014; Published 11 September 2014

Academic Editor: Wenyu Zhang

Copyright © 2014 K. R. Seeja and Masoumeh Zareapoor. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Citations to this Article [6 citations]

The following is the list of published articles that have cited the current article.

  • Aisha Abdallah, Mohd Aizaini Maarof, and Anazida Zainal, “Fraud detection system: A survey,” Journal of Network and Computer Applications, vol. 68, pp. 90–113, 2016. View at Publisher · View at Google Scholar
  • Gayathri, and Umarani, “An efficient misclassification cost concerned imbalanced class handling to improve the financial fraud detection scheme,” Indian Journal of Science and Technology, vol. 9, no. 20, 2016. View at Publisher · View at Google Scholar
  • Aderemi O. Adewumi, and Andronicus A. Akinyelu, “A survey of machine-learning and nature-inspired based credit card fraud detection techniques,” International Journal of System Assurance Engineering and Management, 2016. View at Publisher · View at Google Scholar
  • Doaa Hassan, “The impact of false negative cost on the performance of cost sensitive learning based on bayes minimum risk: A case study in detecting fraudulent transactions,” International Journal of Intelligent Systems and Applications, vol. 9, no. 2, pp. 18–24, 2017. View at Publisher · View at Google Scholar
  • Fabrizio Carcillo, Yann-Aël Le Borgne, Olivier Caelen, and Gianluca Bontempi, “Streaming active learning strategies for real-life credit card fraud detection: assessment and visualization,” International Journal of Data Science and Analytics, 2018. View at Publisher · View at Google Scholar
  • Wei Nai, Lu Liu, Shaoyin Wang, and Decun Dong, “Modeling the Trend of Credit Card Usage Behavior for Different Age Groups Based on Singular Spectrum Analysis,” Algorithms, vol. 11, no. 2, pp. 15, 2018. View at Publisher · View at Google Scholar