A new classification mining model based on the data warehouse

被引:0
作者
Zhang, SL [1 ]
Zhang, JF [1 ]
机构
[1] Taiyuan Heavy Machinery Inst, Dept Comp Sci & Engn, Taiyuan 030024, Peoples R China
来源
2003 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-5, PROCEEDINGS | 2003年
关键词
data warehouse; RST; CLT; classification mining model; algorithm;
D O I
10.1109/ICMLC.2003.1264464
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In this paper, we develop a new classification mining model by merging rough set theory and concept lattice theory according to the data features in the data warehouse. Rough set is a very useful tool which can analysis uncertain and vague data and can be used to classify data with the specific upper and lower approximate set. Concept lattice, an efficient formal analysis tool, can classify the data through the relation of concept intension and concept extension. When faced the integrated, diversity, or capacity data in the data warehouse, merging the two theory will improve the efficiency and reliability of the classification knowledge, which can effectively guide the decision-making management.
引用
收藏
页码:168 / 171
页数:4
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