The rough set theory is a mathematics tool in processing inaccurate, inconsistent and
incomplete problems, which can find the implicit knowledge and potential regulations by
directly analyzing and deducing the data without any prior information except the data set.
The attribute reduction is a important problem of the rough set theory, which delete the
redundant attributes on the condition of keeping on the invariable classifying ability and the
fast algorithm of reduction is one of the main research contents of the theory of rough set,
which is a key step of knowledge acquisition. Therefore, attribute is focused on in this
dissertation. The main content is divided as follows.
(1) Relevant knowledge of the rough set theory is introduced and some classic attribute
reduction algorithms are analyzed systematically.
(2) On the existing attribute reduction algorithm, a modified attribute reduction
algorithm is proposed , which need scan every attribute once in the decision table according
to relative positive region concept in the rough set theory. It enable the attribute value
simplify at the same time.
(3) On the basis of elicitation information which is based on attribute significance,
putting forward seeking nuclear and reduction method based on degree importance of