A problem in the information system and knowledge discovery, is a problem of processing optimal knowledge reduction.
最佳知识约简问题是信息系统与知识发现中面临的一个重要问题。
The process of knowledge discovery in time series includes preprocessing of time series data, attributes reduction and rules extraction.
知识发现的过程包括时间序列数据预处理、属性约简和规则抽取三部分。
So a reduction algorithm of attribute for personalized knowledge discovery was designed.
为此，设计了一种面向个性化知识发现的属性约简算法。
The process of attribute reduction is analyzed in assembly knowledge discovery based on rough set.
分析了基于粗糙集理论的装配知识发现中属性归约的过程。
Attribute reduction is one of the key problems in knowledge discovery.
属性约简是知识发现中的关键问题之一。
Based on the former reduction algorithm, built the knowledge discovery focus system, the system is used in aluminium electrolysis production data analysis, have achieved good results.
在此基础上，开发了基于领域知识的知识发现初始聚焦系统，将该系统应用于铝电解槽生产数据的分析中，取得了良好的效果。
Based on the former reduction algorithm, built the knowledge discovery focus system, the system is used in aluminium electrolysis production data analysis, have achieved good results.
在此基础上，开发了基于领域知识的知识发现初始聚焦系统，将该系统应用于铝电解槽生产数据的分析中，取得了良好的效果。
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