The Application Research Of Rough Set In Data Mining Of Incomplete Information System

In 1982, Polish scholar Z. Pawlak put forward Rough Set theory. It can be utilized as a mathematical tool for the analysis of imprecise and incomplete information with the support of the interrelated data set only. So far plentiful achievements of Rough Set have been made both in theory research and application.Data Mining is a process that can abstract embedded and potentially usefulinformation from large amounts of data that are incomplete, noisy, fuzzy, and random.Rough Set can deal with the data containing incomplete information, which is beyondthe ability of the traditional technology of data mining. As the extension of set theory,one of Rough Set's central research fields is data mining with incomplete information.The dissertation mainly focuses on the research and application of Rough Set in the incomplete information system. It proposes two data reduction models based on discernibility matrix and data analysis separately, improves the discernibility matrix algorithm and made the comparison between the two models.In this dissertation, a data mining model based on Rough Set that is B/S structure is made. And a practical instance about electric power system fault diagnose is brought forward to check this model. It is found that part of the signals in the electric network fault is incomplete or even wrong, the right diagnose can still be obtained through the model. So it is concluded that this model is effective in handling the incomplete and imprecise information.