Evaluation of a Clustering Technique Based on Game Theory
In this paper we focus on the task of clustering in data mining applications.We introduce a formulation of a new clustering algorithm by modelling the system as a cooperative game in strategic form using game theory. The goal is to partition a dataset into k Clusters. Our approach has been applied to both simulated and real-world datasets.In a ddition,we have implemented functions based on the calculation of errors to track both similarity of the data within the same cluster and dissimilarity measure of the data elements between different clusters. Experimental results show that our algorithm is capable of providing a comprehensive description of the final solutions and it has good predictive capabilities.
Data mining clustering Game Theory strategy equilibrium, decision-making
Salima Sabri Mohammed Said Radjef Mohand Tahar Kechadi
Universiy A.MIRAof Bejaia,Road Targua Ouzemour 06000,Bejaia,Algeria CRIL-Universite Lille-Nord de Frace,Artois, CRIL-CNRS UMR 8188,rue Jean Souvraz SP18,F-62307Lens Fra School of Computer Science and Informatics,University College Dublin(UCD),Belfield, Dublin 4, Irelan
国际会议
福州
英文
71-76
2011-06-29（万方平台首次上网日期，不代表论文的发表时间）