An Unsupervised Density Based Clustering Algorithm to Detect Election Anomalies : Evidence from Georgia’s Largest County

Abstract

The 2020 election was fraught with allegations of fraud. To respond to a lack of a robust method to investigate these allegations, we propose a multi-step clustering based approach. We first solve a regression problem to find a group of influential variables, then cluster on these variables to get a set of precincts that should have similar election results. Re-clustering each cluster shows us the outliers. We then apply the approach to Fulton County, Georgia’s largest county and an epicenter of allegations of corruption and fraud. We show that the level of fraud detected is not significant and would not be enough to change the election results in Georgia. In fact, the majority of the precincts that showed to be anomalous were ones where Trump received more votes than was expected. We also validate our analysis through application to the 2015 Argentina National Election.