Study On Attribute Reductiorn Model And Algorithm Of The Set-valued Decision Information System

Rough Set Theory is a powerful theoretical tool which can be used to deal with i uncertain,incomplete and fuzzy knowledge.This theory has been widely applied to various fields such as artificial intelligence,data mining,machine learning and pattern recognition and so on.Due to the data sources' diversity,the objective things' uncertainty and technical limitation,it is sometimes difficult to obtain the accurate data,which leads to the generation of incomplete information systems.As a generalized model of single-valued information system,set-valued information system can be used to handle Incomplete information system.With set-valued decision information system as the object,the current dissertation studies attribute reduction for set-valued decision information system.However,due to the large data age,many data are often dynamic changed,the static reduction algorithm is more difficult to deal with attribute reduction under the dynamic data.Therefore,the research of dynamic data updating methods for rough sets has important practical significance.In this case,we study the effects of incremental updating methods of the attribute sets and the object on attribute reduction in set-valued decision information systems in this paper.Some theoretical problems were mainly researched in the paper as follows,and we got the following innovation results:(1)Aiming at attribute reduction of set-valued decision information system,an attribute reduction algorithm based on distinguishable object set is proposed.The incomplete decision table is transferred into a set-valued decision information system,and then the distribution reduction and the maximum distribution reduction based on similar relation in the set-valued decision information system are analyzed.All results of the distribution reduction and the maximum distribution reduction are calculated by the distinguishable set of objects' sets and the minimum disjunctive normal form.(2)Since the data in set-valued decision information system are usually changed,a heuristic dynamic updating algorithm of attribute reduction is designed by introducing the concepts of conditional information quantity and significance of attribute.When new condition attributes are added to the set-valued decision information system,the proposed algorithm makes use of the attribute reduction results of the old system,updates the attribute reduction results quickly with the variation of attribute set,and delete some redundant attributes in the new result of attribute reduction in reverse order,which can improve the computational efficiency.(3)Since the data in set-valued decision information system are usually changed,in this paper,the effect of newly incremented objects on the old knowledge is studied and the updating mechanism of newly incremented objects on the distributed reduction is analyzed.Then an incremental method for computing the distribution function is proposed.On this basis,an incremental attribute reduction algorithm of the set-valued decision information system is designed.When new objects are added to the set-valued decision system,the proposed algorithm can make use of the reduction results of the old system,and update the distributed coordination set quickly.Then the results attribute reduction are calculated by the minimum disjunctive normal form.