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

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.