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Data Preview ? 537K

    Data Structure ?


    Ahmad Alzahrani and Samira Sadaoui
    alzah234 '@' and sadaouis '@'
    Department of Computer Science
    University of Regina
    Regina, SK, CANADA, S4S 0A2

    Data Set Information:

    Provide all relevant information about your data set.

    Attribute Information:

    Record ID: Unique identifier of a record in the dataset.
    Auction ID: Unique identifier of an auction.
    Bidder ID: Unique identifier of a bidder.
    Bidder Tendency: A shill bidder participates exclusively in auctions of few sellers rather than a diversified lot.  This is a collusive act involving the fraudulent seller and an accomplice.
    Bidding Ratio: A shill bidder participates more frequently to raise the auction price and attract higher bids from legitimate participants.
    Successive Outbidding: A shill bidder successively outbids himself even though he is the current winner to increase the price gradually with small consecutive increments.
    Last Bidding: A shill bidder becomes inactive at the last stage of the auction (more than 90\% of the auction duration) to avoid winning the auction.
    Auction Bids: Auctions with SB activities tend to have a much higher number of bids than the average of bids in concurrent auctions.
    Auction Starting Price:  a shill bidder usually offers a small starting price to attract legitimate bidders into the auction.
    Early Bidding: A shill bidder tends to bid pretty early in the auction (less than 25\% of the auction duration) to get the attention of auction users.
    Winning Ratio: A shill bidder competes in many auctions but hardly wins any auctions.
    Auction Duration:  How long an auction lasted.
    Class: 0 for normal behaviour bidding; 1 for otherwise.

    Relevant Papers:

    Paper 1: Scraping and Preprocessing Commercial Auction Data for Fraud Classification
    Paper 2: Clustering and Labeling Auction Fraud Data

    Citation Request:

    Alzahrani A, Sadaoui S. Scraping and preprocessing commercial auction data for fraud classification. arXiv preprint [Web link]. 2018 Jun 2.
    Alzahrani A, Sadaoui S. Clustering and labeling auction fraud data. InData Management, Analytics and Innovation 2020 (pp. 269-283). Springer, Singapore.