  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.
