NDLI: Online bidding system based on Cournot model using K-means clustering

Content Provider	IEEE Xplore Digital Library
Author	Jun Tan Yan-Jiang Jia
Copyright Year	2014
Description	Author affiliation: Sch. of Math. & Comput. Sci., Sun Yat-Sen Univ., Guangzhou, China (Jun Tan) \|\| Bidding Manage. Center, Sun Yat-Sen Univ. Guangzhou, Guangzhou, China (Yan-Jiang Jia)
Abstract	Online E-commence has been growing rapidly in the world. Online bidding has become popular. This work will come out with a model of online bidding system (OBS). This paper propose a new model to deal with online bidding, we deal with auction with Cournot Bidding Data Mining (CBDM). CBDM framework based on Cournot model is designed for K-means clustering. The input auction space is partitioned into groups of similar auctions by K-means clustering algorithm. The problem of finding the value of k in K-means algorithm is solved by method using Cournot competition. Cournot bidding is employed to obtain the optimal bidding strategies for the current auction. The clustering algorithm has been deployed successfully into online bidding, yielding significant improvement in performance over the existing OBS.
Starting Page	363
Ending Page	368
File Size	216170
Page Count	6
File Format	PDF
e-ISBN	9781479951482
DOI	10.1109/FSKD.2014.6980861
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-08-19
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Procurement Computational modeling Clustering algorithms Abstracts Educational institutions Data mining Optimization
Content Type	Text
Resource Type	Article

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