Data Mining Study Based On Rough Sets And Fuzzy Neural Network

With the development of the Database technology and Internet, it is possible to collect and store massive quantities of data. When facing the greatly expanding data, people often feel hard to understand data and even harder to extract valuable knowledge from data. People are drowning in data, but starving for knowledge. Traditional information processing techniques are now not adaptive to practical applications. People need more powerful and more efficient information processing techniques, which can discover interesting knowledge from massive information, to guide to making decisions. So data mining technology, which is an effective approach to resolve the problem of abundant data and scanty information, came into being. It currently is the research frontier within the information science field. The related researches and applications have greatly improved the ability for decision supporting. It has been deemed to a field that has broad prospect of application in database research.Rough Set is a tool to deal with vague and uncertain data; therefore it becomes an important frame in data mining. Reduction of knowledge is one of the core contents of Rough Set theory. Data after reduction is more valuable and can obtain more accurate knowledge. The application of Rough Set theory in data mining field can greatly improve the ability to analyze and study incomplete data in massive database, which has broad prospect of application and practical value. Reduction of attribute is another important subject in Rough Set theory. There is much abundant and unnecessary attribute for discovering rules in massive database. If we can delete abundant attribute, we will raise the clarity of potential knowledge in the system, reduce the complexity of time for discovering rules and increase the efficiency of discovering.Fuzzy system can express knowledge and reference like human thought. However it unduly depends on experts'knowledge and lacks the ability to study and adapt. Neural network structure is variable, is capable of self-organizing, self-studying, un-linear, and containing mistakes. But it is lacking of ability to express and explain knowledge. Meanwhile network parameter lacks physical meaning, which makes it easy to fall into partial extreme value during study process. So it becomes necessary trend to combine fuzzy system and neural network. Thus Fuzzy Neural Network was...