Research Of Attribute Reduction Based On Binary Discernable Matrix

Data Mining is the process of mining the interesting, potentially useful, and understandable knowledge in data. Classification is an important sub-branch of Data Mining and the method of Rough Set is one of the important techniques of Classification. Rough Set is a new mathematical tool to deal with fuzzy and uncertain knowledge. It has strong knowledge obtaining ability. It is the new research domain that the theories of Rough Set are applied in Data Mining. Because the theories of Rough Set provide the strict mathematics method of the problem of dealing with datas Classification, without the additional information of any datas, they can search for the smallest assembly of the datas , may use datas of determining the nature and fixing quantity and also can generate the rule assembly of policy decision from the datas,etc. Rough Set has got the extensive application.Not all the condition attributes are necessary for classification, some are unnecessary. Doing away with these attributes won't affect original classification effect, on the contrary, it will improve the articulation of the latent knowledge in system. Attribute reduction is reducing condition attributes in a decision table .The decision table after being reduced has the function of the one before being reduced,but the former has the less condition attributes .The dissertation is mainly researching on Binary Discernable Matrix in the theories of Rough Set and related theories and calculation formulas based on the knowledge granulation. We can calculate the discernment degree, granulation of knowledge and attribute significance making use of formulas obtained. We can also go on attribute reduction and value reduction of the decision table utilizing related formulas obtained and present two reduction algorithms:one is attribute and its value reduction algorithm based on Binary Discernable Matrix.Only scanning Binary Discernable Matrix once, the algorithm can get core attributes and objects that can't be classified correctly, so we can obtain core values. Reduction assemblies of condition attributes can be acquired if we apply the role of absorptive to every disjunct normal form, so we can get a core value table having attributes which are reduced. The algorithm makes that attribute reduction and its value reduction are calculated identically, so it shortens time of reduction greatly.The other is the attribute and its value reduction algorithm based on attribute significance of Binary Discernable Matrix(BDMSR):on the basic of getting core attributes,the smallest reduction assembly of a decision table is formed by increasing a attribute one by one utilizing attribute significance of the Binary Discernable Matrix as a criterion of attribute selection. The algorithm makes that attribute reduction and attribute value reduction are calculated identically.too.In addition, we designed a prototype system based on BDMSR algorithm model and attribute reduction algorithm model based on Binary Discernable Matrix(BDMR). On this uniform flatform, we compared the BDMSR algorithm and BDMR algorithm by using the standard UCI data sets. From the experiment, we can see the BDMSR algorithm is superior to BDMR algorithm indeed.