K-Means Algorithm for Recognizing Fraud Users on a Bitcoin Exchange Platform

Authors

Yanfeng Wang, School of Business Administration, South China University of Technology, ChinaFollow
Feng Li, School of Business Administration, South China University of Technology, ChinaFollow
Jinya Hu, School of Business Administration, South China University of Technology, ChinaFollow
Dong Zhuang, School of Business Administration, South China University of Technology, ChinaFollow

Corresponding Author

Yanfeng Wang

Document Type

Article

Abstract

This paper addresses recognizing fraud users on a Bitcoin exchange website-bitcoin-otc. According to online rating records provided by the website, some users behave significantly different from others. Seeing that, the classical K-means clustering algorithm is proposed to identify these abnormal users. K-means algorithm is an unsupervised clustering algorithm that clusters users based on feature similarity. Therefore, performance of K-means algorithm relies on the features. This paper explored and found the best collection of features based on real record data, e.g., mean of total ratings sent. Since the selected features are not observed for record set, the website should offer these features for potential traders.

Recommended Citation

Wang, Yanfeng; Li, Feng; Hu, Jinya; and Zhuang, Dong, "K-Means Algorithm for Recognizing Fraud Users on a Bitcoin Exchange Platform" (2018). ICEB 2018 Proceedings (Guilin, China). 59.
https://aisel.aisnet.org/iceb2018/59