Data Mining Driven agents for Predicting Online Auction's End Price

Publisher:
IEEE Computational Intelligence Society
Publication Type:
Conference Proceeding
Citation:
2011 IEEE Symposium Series on Computational Intelligence Proceedings, 2011, pp. 141 - 147
Issue Date:
2011-01
Full metadata record
Files in This Item:
Filename Description Size
Thumbnail2010005495OK.pdf Published version5.13 MB
Adobe PDF
Abstract Auctions can be characterized by distinct nature of their feature space. This feature space may include opening price, closing price, average bid rate, bid history, seller and buyer reputation, number of bids and many more. In this paper, a clustering based method is used to forecast the end-price of an online auction for autonomous agent based system. In the proposed model, the input auction space is partitioned into groups of similar auctions by kmeans clustering algorithm. The recurrent problem of finding the value of k in k-means algorithm is solved by employing elbow method using one way analysis of variance (ANOVA). Then k numbers of regression models are employed to estimate the forecasted price of an online auction. Based on the transformed data after clustering and the characteristics of the current auction, bid selector nominates the regression model for the current auction whose price is to be forecasted. Our results show the improvements in the end price prediction for each cluster which support in favor of the proposed clustering based model for the bid prediction in the online auction environment.
Please use this identifier to cite or link to this item: