| 摘要(英) |
In this thesis, we proposed a modified attribute oriented induction method to find rules which can predict close price (the actual price paid by winner) and the duration (from the beginning of auction to the last bid) of Business-to-Business auctions exercised on a commercial website. The uncertainty faced both sellers and buyers is an inherent feature of auctions. While a seller is anxious for knowing the actual selling price, a Bidder has very short time to decide to bid or not to bid at this moment, especially when a little difference may cause very big loss or profit to a company. Even the provider of the e-marketplace also cares about whether this is an efficient auction when the close price should be higher (lower) than the reserve price in a forward (backward) auction, how long the auction would be taken and when the employees should come back to handle the results. Therefore, how fast one can predict the outcome of the auction and how accurate the prediction is became a critical problem.
We focused on two important variables of auction, time and price, and collected auction data of backward English auction format of three buyers which represent three distinct companies from a B-to-B auction e-marketplace provider in the past two years. The rules summarized from datasets by our modified attribute oriented induction can estimate the predicted close price and duration after several bids are taken when an auction opened |
| 參考文獻 |
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