Decision analysis of data mining project based on Bayesian risk

Authors:
Guangli Nie;Lingling Zhang;Ying Liu;Xiuyu Zheng;Yong Shi
Affiliations:
School of Management, Graduate University of Chinese Academy of Sciences, Beijing 100080, China and Research Center on Fictitious Economy and Data Science, CAS, Beijing 100080, China;School of Management, Graduate University of Chinese Academy of Sciences, Beijing 100080, China and Research Center on Fictitious Economy and Data Science, CAS, Beijing 100080, China;Research Center on Fictitious Economy and Data Science, CAS, Beijing 100080, China;School of Management, Graduate University of Chinese Academy of Sciences, Beijing 100080, China and Research Center on Fictitious Economy and Data Science, CAS, Beijing 100080, China;Research Center on Fictitious Economy and Data Science, CAS, Beijing 100080, China
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

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Abstract

Data mining, an efficient method of business intelligence, is a process to extract knowledge from large scale data. As the augment of the size of enterprise and the data, data mining as a way to make use of the data become more and more necessary. But now most of the literatures only focus on the algorithm itself. Few literatures research what qualification to fulfill before the decision doing data mining from the perspective of the company manager. This paper discusses the factors affect the data mining project. Based on the Bayesian risk, we build a model taking the risk attitude of the top executive in account to help them make decision whether to do data mining or not.