Mining Method Based On Association Rules Based On Rough Set Theory

Rough set theory, initialized by Professor Pawlak in early 1980's,has been proved to be an excellent mathematical tool dealing with uncertain and vague description of objects, whose basic idea is to derive classification rules of conception by knowledge reduction with the ability of classification unchanged. It may find the hiding and potential rules, that is knowledge, from the data without any preliminary or additional information. In recent years, as an important part of soft computing, rough set theory and its applications have played an important role, especially in the areas of pattern recognition, machine learning, decision analysis, knowledge discovery and knowledge acquisition etc.Firstly, the classical Pawlak rough sets based on the complete information system and the equivalence relation is introduced, which approximates sets of object by upper and lower set approximations. But there exist some limits, one generalized rough set models are introduced: incomplete information rough set model; Secondly, the problem of attribute reduction and rule extract are discussed, the problem of attribute reduction has been proved to be NP-Hard to find all reductions and a minimal reduction. Hence, it is helpful to use different decision tree algorithms to find a set of decision rules. In practical apply, the rules must are satisfied some conditions which we pay attention to, and the process must be simple in compute. In order to achieve those, an algorithm with a restrict condition was provide in this article. Thirdly, the decision rule algorithm is introduced, it is a very simple method on decision rules extract. Integrate the rough set theory to the decision tree algorithm. A new decision rules extract method- the decision tree algorithm based on rough set theory with two restrict conditions was provided. And gives an example was given prove that this algorithm is effective. At last, incomplete information rough set mode was studied. Based on the achievements in the past, a method extract rules on incomplete information was provided. Which can not only draw decision rules but also determine the miss information at the same time. And it is simple in compute relatively.