The Research Of Algorithm Of Bayesian Networks Used In Data Mining

With database and Internet used increasingly and the advanced tools of building and collecting data automatically used widely, the amount of data people hold has been increasing rapidly, and the ability of utilizing information technique and searching data has been improving .How to utilize and deal with the information availably has become a focus all over the world concerned. Database technology, AI and Statistics have been developing and integrating. As a result, Data mining(DM) emerged as the times require. It is a new subject and the necessary result modern science and technology penetrated each other. The basic object of DM is to distill knowledge and information which are concealed, potential and useful from a great deal of data. The technology has been absorbing many experts and used widely in many fields, for example, Finance, Retail, medical treatment and government decision-making since the end of the 20th century. Moreover it has acquired good social benefit and economy benefit, and it will have broad perspective of exploiture and application.Bayesian Network is an outcome probability and map combines and a means which can denote knowledge, reason, learn. It can accomplish decision-making, diagnosis, forecast and classifying, and it has become an important method, which is comprehensible and logical.In this dissertation I dedicate to the research of Bayesian Network's theory and algorithms .The entire thesis can be divided into three parts.1. Bayesian Networks and Data Mining combine. Because of data mining's strong statistic characteristic ,as well as Bayesian Networks originating from Bayesian statistics, it causes Bayesian Networks combined with data mining.2. The reasoning technology of Bayesian Networks. Through inferring in networks, we can find out the dependence of two nodes to confirm relation of affairs denoted by nodes, at the same time, to predict the development of these affairs. To different networks, we adopt different inference algorithms to quicken the rate of reasoning and improve calculation efficiency. Because Junction tree algorithm is easy to understand and its range of application is broad , it has been the most used widely among the exact inference algorithms.3. The learning of Bayesian Networks. The learning of Bayesian Networks is an important tache, which combines training data with prior knowledge and model evaluation to acquire the structure hidden in data and parameters. The learning of Bayesian Networksincludes structure learning and parameter learning and structure learning is the core of it. Discuss the learning principles, processes and computational complexity of three phases structure learning algorithm and through typical database validate the algorithm. Parameter learning has two parts: learning from incomplete data and complete data. To different situation, we adopt different learning algorithms to learn condition probability tables (CPT) of Bayesian Network from data.Bayesian principle;Bayesian Network;prior knowledge;probability reasoning;Junction tree algorithm;Three phases algorithm.