Name
Abstract | ||
|---|---|---|
Identifying bidders with suspicious bidding activities related to possible online auction fraud is a difficult task due to a large number of users participating in online auctions. In order to reduce the number of users to be investigated, we examine observable features of a bidder's behavior, and utilize a hierarchical clustering technique to divide a collection of bidders into normal and deviant groups. Based on the clustering results, we generate a decision tree that can be used to efficiently characterize new bidders as normal, suspicious, or highly suspicious. To illustrate the effectiveness of our proposed approach, we collected real auction datasets from online auctions, and used 3-fold validation approach to show that the error rates of the generated decision trees are reasonably low. |
Year | Venue | Keywords |
|---|---|---|
2010 | IC-AI | online auctions,decision tree.,hierarchical clustering,shilling behavior,suspicious bidder,error rate,decision tree |
Field | DocType | Citations |
Hierarchical clustering,Decision tree,Data mining,Computer science,Common value auction,Artificial intelligence,Conceptual clustering,Cluster analysis,Brown clustering,Bidding,Online auction,Machine learning | Conference | 7 |
PageRank | References | Authors |
0.51 | 4 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Benjamin J. Ford | 1 | 106 | 7.92 |
| Haiping Xu | 2 | 385 | 42.47 |
| Iren Valova | 3 | 136 | 25.44 |