Clustering and Labelling Auction Fraud Data
This work addresses auction fraud detection for e-commerce platforms, but it is incremental as it applies an existing clustering method to a new dataset.
The authors tackled the problem of detecting shill bidding fraud in auctions by building a high-quality labeled dataset from eBay data, using hierarchical clustering (CURE) to label instances, which achieved remarkable results in experiments.
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.