Pricing analysis in online auctions using clustering and regression tree approach

Kaur, P; Goyal, M; Lu, J

Pricing analysis in online auctions using clustering and regression tree approach

Kaur, P Goyal, M

Lu, J

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012, 7103 LNAI pp. 248 - 257
Issue Date:: 2012-01-01

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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	2012-01-01	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012, 7103 LNAI pp. 248 - 257	en_US
dc.identifier.isbn	9783642276088	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/19101
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 price forecasting agent (PFA) is proposed using data mining techniques 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). Based on the transformed data after clustering, bid selector nominates the cluster for the current auction whose price is to be forecasted. Regression trees are employed to predict the end-price and designing the optimal bidding strategies for the current auction. Our results show the improvements in the end price prediction using clustering and regression tree approach.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.relation.isbasedon	10.1007/978-3-642-27609-5_16	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Pricing analysis in online auctions using clustering and regression tree approach	en_US
dc.type	Conference Proceeding
utslib.citation.volume	7103 LNAI	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Taipei, Taiwan	en_US
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	open_access
pubs.publication-status	Published	en_US
pubs.volume	7103 LNAI	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 price forecasting agent (PFA) is proposed using data mining techniques 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). Based on the transformed data after clustering, bid selector nominates the cluster for the current auction whose price is to be forecasted. Regression trees are employed to predict the end-price and designing the optimal bidding strategies for the current auction. Our results show the improvements in the end price prediction using clustering and regression tree approach.

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http://hdl.handle.net/10453/19101