Knowledge-oriented clustering for decision support

被引：0
|
作者：
Bean, CL [1 ]
Kambhampati, C [1 ]
机构：
[1] Univ Hull, Dept Comp Sci, Neural Emergent & Agent Technol Grp, Kingston Upon Hull HU6 7RX, N Humberside, England
来源：
PROCEEDINGS OF THE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS 2003, VOLS 1-4 | 2003年
关键词：
Rough Set Theory; knowledge-oriented clustering; ambiguity; decision support;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Cluster Analysis is traditionally an unsupervised data reduction technique. However, by unifying ideas from both Cluster Analysis and Rough Set Theory, the inherent structure of a data set can be expressed as a rule set which, in turn, can be modified in a supervised manner to obtain a decision rule set with minimal ambiguity. This process of encasing knowledge extraction in an algorithmic framework results in an optimal process for decision support.
引用
收藏
页码：3244 / 3249
页数：6
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