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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Zhang, S., Jank, W., Shmueli, G.: Real-time forecasting of online auctions via functional K-nearest neighbors. International Journal of Forecasting (2010)
Ghani, R., Simmons, H.: Predicting the end-price of online auctions. In: Proceedings of the International Workshop on Data Mining and Adaptive Modeling Methods for Economics and Management, Held in Conjunction with the 15th European Conference on Machine Learning (2004)
Guo, W., Chen, D., Shih, T.: Automatic forecasting agent for e-commerce applications. In: 20th International Conference on Advanced Information Networking and Applications (AINA 2006), pp. 1–4 (2006)
Kehagias, D.D., Mitkas, P.A.: Efficient E-Commerce Agent Design Based on Clustering eBay Data. In: IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology Workshops, pp. 495–498 (2007)
Xuefeng, L., Lu, L., Lihua, W., Zhao, Z.: Predicting the final prices of online auction items. Expert Systems with Applications 31, 542–550 (2006)
Heijst, D., Potharst, R., Wezel, M.: A support system for predicting ebay end prices. Econometric Institute Report (2006)
Nikolaidou, V., Mitkas, P.A.: A Sequence Mining Method to Predict the Bidding Strategy of Trading Agents. In: Cao, L., Gorodetsky, V., Liu, J., Weiss, G., Yu, P.S. (eds.) ADMI 2009. LNCS, vol. 5680, pp. 139–151. Springer, Heidelberg (2009)
Jank, W., Shmueli, G.: Profiling price dynamics in online auctions using curve clustering. Technical report, Smith School of Business, University of Maryland, pp. 1–28 (2005)
Greenwald, A., Stone, P.: Autonomous bidding agents in the trading agent competition. IEEE Internet Computing, 52–60 (2001)
Byde, A., Preist, C., Jennings, N.: Decision procedures for multiple auctions. In: ACM First International Joint Conf. on Autonomous Agents and Multi-Agent Systems, pp. 613–620 (2002)
Anthony, P., Jennings, N.: Evolving bidding strategies for multiple auctions. In: Proc. of 15th European Conf. Artificial Intelligence, pp. 178–182 (2002)
Cao, L., Gorodetsky, V., Mitkas, P.A.: Agent Mining: The Synergy of Agents and Data Mining. IEEE Intelligent Systems 24(3), 64–72 (2009)
Goyal, M., Kaushik, S., Kaur, P.: Automated Fuzzy Bidding Strategy for Continuous Double Auctions Using Trading Agents Attitude and Market Competition. International Journal of Agent Technologies and Systems 2(4) (2010)
Min, Z., Qiwan, W.: The On-line Electronic Commerce Forecast Based on Least Square Support Vector Machine. In: Second International Conference on Information and Computing Science, ICIC 2009, vol. 2, pp. 75–78 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kaur, P., Goyal, M., Lu, J. (2012). Pricing Analysis in Online Auctions Using Clustering and Regression Tree Approach. In: Cao, L., Bazzan, A.L.C., Symeonidis, A.L., Gorodetsky, V.I., Weiss, G., Yu, P.S. (eds) Agents and Data Mining Interaction. ADMI 2011. Lecture Notes in Computer Science(), vol 7103. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27609-5_16
Download citation
DOI: https://doi.org/10.1007/978-3-642-27609-5_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-27608-8
Online ISBN: 978-3-642-27609-5
eBook Packages: Computer ScienceComputer Science (R0)