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

Author

Jun Tan ; Yan-Jiang Jia

Author_Institution

Sch. of Math. & Comput. Sci., Sun Yat-Sen Univ., Guangzhou, China

fYear

2014

fDate

19-21 Aug. 2014

Firstpage

363

Lastpage

368

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.

Keywords

data mining; electronic commerce; pattern clustering; tendering; CBDM framework; Cournot bidding data mining; Cournot competition; Cournot model; K-means clustering; OBS; input auction space partitioning; online bidding system; online e-commence; optimal bidding strategies; Abstracts; Clustering algorithms; Computational modeling; Data mining; Educational institutions; Optimization; Procurement;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems and Knowledge Discovery (FSKD), 2014 11th International Conference on

Conference_Location

Xiamen

Print_ISBN

978-1-4799-5147-5

Type

conf

DOI

10.1109/FSKD.2014.6980861

Filename

6980861