Abstract: Measure of knowledge dependency and attribute significance is an important issue in rough set theory.It has been widely applied to knowledge reduction,rule extraction,etc.Classical measure of knowledge dependency and attri-bute significance has a littie bit of limitation in dealing with data,and sometimes it cannot obtain precise and reasonable results,which leads to a series of deviations in the subsequent applications.To this end,through deep analysis of the exi-sting classical knowledge dependency,combining it with the majority inclusion relation,and adding credibility parameters,a new method of measuring knowledge dependency and attribute significance was proposed.Finally,the new mea-sure method was applied to decision information system.And the analysis results show that the new method is effective.

Key words: Rough set,Decision information system,Knowledge dependency,Attribute significance,Measure