Decision procedures for multiple auctions
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 2
Autonomous Bidding Agents in the Trading Agent Competition
IEEE Internet Computing
Automatic Forecasting Agent for e-Commerce Applications
AINA '06 Proceedings of the 20th International Conference on Advanced Information Networking and Applications - Volume 02
Efficient E-Commerce Agent Design Based on Clustering eBay Data
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ICIC '09 Proceedings of the 2009 Second International Conference on Information and Computing Science - Volume 02
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IEEE Intelligent Systems
A Sequence Mining Method to Predict the Bidding Strategy of Trading Agents
Agents and Data Mining Interaction
International Journal of Agent Technologies and Systems
A multi-agent data mining system for cartel detection in Brazilian government procurement
Expert Systems with Applications: An International Journal
Price forecasting using dynamic assessment of market conditions and agent's bidding behavior
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part I
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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.