Data mining based on rough sets in risk decision-making: foundation and application

  • Authors:

  • Li Wanqing;Ma Lihua;Wei Dong

  • Affiliations:

  • School of Economics and Management, Hebei University of Engineering, Handan, China;School of Economics and Management, Hebei University of Engineering, Handan, China;School of Economics and Management, Hebei University of Engineering, Handan, China

  • Venue:
  • WSEAS Transactions on Computers
  • Year:
  • 2010

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Abstract

In order to solve the problem of the redundant information to distinguish in the risk decision-making, in this paper, the data mining algorithms based on Rough Sets is studied. And we know the risk decision-making is an important aspect in the management practice. In the risk decision process of a project decision-making, it is necessary to use the algorithm to discover valuable knowledge and make a right decision. In the paper, a data mining method called Rough Sets is introduced in the field. And the algorithmic process of data mining based on Rough Set is studied. According to the Rough Sets theory, firstly, the factors set is established including condition attribute and decision attribute. Secondly, experts qualitatively describe risk factors and establish a decision database, called decision table. Thirdly, the attribute reduction algorithm based on Rough Sets is used to eliminate the redundant risk factor and its value of decision table. Fourthly, the minimum decision rules are abstracted based on data mining technology. Finally, the process of risk decision based on data mining of Rough Sets is analyzed in a case study.