Comparative Study of k-Nearest Neighbor (KNN) and k-Means Algorithm in Fraud Detection

Authors

  • Jendi Bade Shrestha Nepal College of Information Technology, Nepal
  • Suresh Pokharel Nepal College of Information Technology, Nepal

DOI:

https://doi.org/10.3126/jost.v3i1.69064

Keywords:

K-Nearest Neighbor, K-Means, classification, clustering

Abstract

Fraud detection especially in credit card is one of the challenging issues in these days. Finding irregularities is even more difficult due to high volume of data during the transaction. Many data mining techniques are applied by researchers for solving these problems. In this research, we explore K-Nearest Neighbor (KNN) and k-means algorithms which are widely used classification and clustering algorithms respectively. These algorithms are used in this research to find out the better among them. Moreover, we also optimize the system by finding the most dominant and influencing factor responsible for fraud which will help in effective fraud detection in case of credit card.

Downloads

Download data is not yet available.
Abstract
63
PDF
54

Downloads

Published

2023-12-31

How to Cite

Shrestha, J. B., & Pokharel, S. (2023). Comparative Study of k-Nearest Neighbor (KNN) and k-Means Algorithm in Fraud Detection. Journal of Science and Technology, 3(1), 40–45. https://doi.org/10.3126/jost.v3i1.69064

Issue

Section

Articles