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Correlativity sets based theoretical frameworks of data mining
Wang XF(王晓峰); Lu, Jing; Wang TR(王天然)
作者部门先进制造技术研究室
会议名称2003 International Conference on Machine Learning and Cybernetics
会议日期November 2-5, 2003
会议地点Xi'an, China
会议主办者IEEE SMCTC; Hebei Univeristy; Northwestern Polytechnical University
会议录名称International Conference on Machine Learning and Cybernetics
出版者IEEE
出版地NEW YORK
2003
页码188-193
收录类别EI ; CPCI(ISTP)
EI收录号2004128071010
WOS记录号WOS:000189420700039
产权排序2
ISBN号978-0-7803-7865-0
摘要

The plausibility relation, one is generalization of fuzzy relation and probabilistic relation, is proposed in the paper. Data mining is a process of finding the plausibility relation from database and correlativity measure to be a particular plausibility relation based on correlativity sets. The critical calculation such as the accuracy of the rough sets, the confidence and the Bayesian form in data mining can be united which use the correlativity measure. The GPDM (General Process of Data Mining) represented the nature of data mining is proposed also. The data mining theoretical foundation and frameworks based on correlativity sets are given and discussed also in the paper.

语种英语
引用统计
文献类型会议论文
条目标识符http://ir.sia.cn/handle/173321/19942
专题工业信息学研究室_先进制造技术研究室
通讯作者Wang XF(王晓峰)
作者单位1.Shenyang Institute of Chemical Technology, 110142 Shenyang, China
2.Shenyang Institute of Automation, Chinese Academy of Sciences, 110030 Shenyang, China
推荐引用方式
GB/T 7714
Wang XF,Lu, Jing,Wang TR. Correlativity sets based theoretical frameworks of data mining[C]//IEEE SMCTC; Hebei Univeristy; Northwestern Polytechnical University. NEW YORK:IEEE,2003:188-193.
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