Data mining driven agents for predicting online auction's end price

Kaur, P; Goyal, M; Lu, J

Data mining driven agents for predicting online auction's end price

Kaur, P Goyal, M

Lu, J

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Publication Type:: Conference Proceeding
Citation:: IEEE SSCI 2011: Symposium Series on Computational Intelligence - CIDM 2011: 2011 IEEE Symposium on Computational Intelligence and Data Mining, 2011, pp. 141 - 147
Issue Date:: 2011-08-11

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Field	Value	Language
dc.contributor.author	Kaur, P	en_US
dc.contributor.author	Goyal, M https://orcid.org/0000-0003-2853-9393	en_US
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732	en_US
dc.date.issued	2011-08-11	en_US
dc.identifier.citation	IEEE SSCI 2011: Symposium Series on Computational Intelligence - CIDM 2011: 2011 IEEE Symposium on Computational Intelligence and Data Mining, 2011, pp. 141 - 147	en_US
dc.identifier.isbn	9781424499274	en_US
dc.identifier.uri	http://hdl.handle.net/10453/32356
dc.description.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 k-means 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. © 2011 IEEE.	en_US
dc.relation.ispartof	IEEE SSCI 2011: Symposium Series on Computational Intelligence - CIDM 2011: 2011 IEEE Symposium on Computational Intelligence and Data Mining	en_US
dc.relation.isbasedon	10.1109/CIDM.2011.5949427	en_US
dc.title	Data mining driven agents for predicting online auction's end price	en_US
dc.type	Conference Proceeding
utslib.for	0804 Data Format	en_US
dc.location.activity	Paris, France
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

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 k-means 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. © 2011 IEEE.

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http://hdl.handle.net/10453/32356