公开数据集
数据结构 ? 537K
Data Structure ?
* 以上分析是由系统提取分析形成的结果,具体实际数据为准。
README.md
Ahmad Alzahrani and Samira Sadaoui
alzah234 '@' uregina.ca and sadaouis '@' uregina.ca
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.
帕依提提提温馨提示
该数据集正在整理中,为您准备了其他渠道,请您使用
- 分享你的想法
全部内容
数据使用声明:
- 1、该数据来自于互联网数据采集或服务商的提供,本平台为用户提供数据集的展示与浏览。
- 2、本平台仅作为数据集的基本信息展示、包括但不限于图像、文本、视频、音频等文件类型。
- 3、数据集基本信息来自数据原地址或数据提供方提供的信息,如数据集描述中有描述差异,请以数据原地址或服务商原地址为准。
- 1、本站中的所有数据集的版权都归属于原数据发布者或数据提供方所有。
- 1、如您需要转载本站数据,请保留原数据地址及相关版权声明。
- 1、如本站中的部分数据涉及侵权展示,请及时联系本站,我们会安排进行数据下线。