# Clustering and Labelling Auction Fraud Data

arXiv: https://arxiv.org/abs/1808.07288 · HTML: https://mlsanity.com/paper/1808.07288/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Aug 2018.

## Built on by (most cited first, 1 of 1)

- [Efficient Convex Algorithms for Universal Kernel Learning](https://mlsanity.com/paper/2304.07472/) (2023)

## Abstract (arXiv)

Although shill bidding is a common auction fraud, it is however very tough to detect. Due to the unavailability and lack of training data, in this study, we build a high-quality labeled shill bidding dataset based on recently collected auctions from eBay. Labeling shill biding instances with multidimensional features is a critical phase for the fraud classification task. For this purpose, we introduce a new approach to systematically label the fraud data with the help of the hierarchical clustering CURE that returns remarkable results as illustrated in the experiments.