Correlativity sets based theoretical frameworks of data mining

X. F. Wang, Jing Lu, T. R. Wang

Research output: Published contribution to conferencePaperpeer-review

Abstract

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.
Original languageEnglish
Pages188-193
Number of pages6
Publication statusPublished - 2003
Externally publishedYes
EventSecond International Conference on Machine Learning and Cybernetics - Xi'an, China
Duration: 2 Nov 20035 Nov 2003

Conference

ConferenceSecond International Conference on Machine Learning and Cybernetics
Country/TerritoryChina
CityXi'an
Period2/11/035/11/03

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