Correlativity sets based theoretical frameworks of data mining

Author

Wang, Xiao-feng ; Lu, Jing ; Wang, Tian-Ran

Author_Institution

Shenyang Inst. of Chem. Technol., China

Volume

1

fYear

2003

fDate

2-5 Nov. 2003

Firstpage

188

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 set, 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.

Keywords

belief networks; data mining; inference mechanisms; rough set theory; uncertainty handling; Bayesian form; correlativity sets; data mining general process; fuzzy relation; plausibility relation; probabilistic relation; rough set; theoretical frameworks; Bayesian methods; Biomedical measurements; Chemical technology; Companies; Data mining; Databases; Decision trees; Machine learning; Neural networks; Rough sets;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2003 International Conference on

Print_ISBN

0-7803-8131-9

Type

conf

DOI

10.1109/ICMLC.2003.1264468

Filename

1264468