改进了一种粗糙集决策表的值约简算法，并将其应用到文本分类规则的提取中。
A reduction algorithm based on rough set is improved and then applicated to extract the rules of text categorization.
知识约简是其中的核心内容，是在保持分类能力基本不变的情况下，获得系统的约简属性和分类规则。
Knowledge reduction is the core of Rough set. It obtains the reduction attribute and classification rules while holding the ability of classification unchanged.
粗糙集中决策表约简也就是以基于最少的条件属性和最小冗余的属性值导出最少的决策规则或分类规则。
Simplification of Decision tables in Roouh set is order to lead decision rule or categorised rule at the least on the basis of the least condition attribute and minimum redundant attribute value.
包括数据的分类，KRS的建立，数据的约简和决策规则的生成。
It shows how to classify the data, build the KRS, reduce the data and get the final decision rules.
然后采用粗糙集的值约简算法来进行文本分类规则的抽取，从而得到最终的文本分类规则。
Using rough set of the final value reduction algorithm for text classification rules extraction, thus gained the final text classification rules.
运用属性约简算法生成分类规则，最后利用多知识库进行文档分类。
Generating classification rule by attribution reduces algorithm. Finally, the documents are classified with multiple knowledge bases.
粗糙集理论的主要思想是在保持分类能力的前提下通过属性约简和值约简提取的决策规则。
The main idea of rough set theory is to extract decision rules by attribute reduction and value reduction in the premises of keeping the ability of classification.
粗糙集理论的主要思想是在保持分类能力的前提下通过属性约简和值约简提取的决策规则。
The main idea of rough set theory is to extract decision rules by attribute reduction and value reduction in the premises of keeping the ability of classification.
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