A Performance Analysis of Detecting Credit Card Fraud by using CT18 Method
S. Subbulakshmi1, D.J. Evanjaline2
1S. Subbulakshmi, PG and Research Department of Computer Science, Rajah Serfoji Govt. Arts College (Autonomous), Thanjavur, Tamilnadu, India.
2Dr.D.J. Evanjaline, Assistant Professor, PG and Research Department of Computer Science, Rajah Serfoji Govt. Arts College (Autonomous), Thanjavur, Tamilnadu, India.
Manuscript received on November 20, 2019. | Revised Manuscript received on November 28, 2019. | Manuscript published on 30 November, 2019. | PP: 7257-7360 | Volume-8 Issue-4, November 2019. | Retrieval Number: D5280118419/2019©BEIESP | DOI: 10.35940/ijrte.D5280.118419
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Credit cards are a significant component of everyday life. Whether purchasing gas and supermarket stores or reserving a hotel and lease a car for the next holiday. Credit cards are a pleasant and safe type of client payment. Advantages that differ from harm security on payments to the convenience of disputing suspect fees or suspicious activity make credit cards such an appealing form of transaction. It takes an hour for any time activities, online shopping, and paperless system. As the amount of credit card customers rises day by day, significant illegal activities eventually enhance. CT18 technique is the procedure for categorizing information directed at reformatting observations into CT18, whereby each observation belongs to the closest mean cluster. This is one of the simplest unsupervised learning algorithms that solve the well-known grouping problem.
Keywords: Adaboost, Naïve Bayes, Credit Card,CT18 Algorithm.
Scope of the Article: Performance Evaluation of Networks.