Mass Of Scientific Data Mining Based On Bayesian Theory

With the application of Database and the development of Internet, accumulated data are exponential increasing. For these data people are not satisfied with the traditional methods of queries and statistics, but want to find deeper regulations to provide effective decision to science and research works. So data mining technology that apply machine learning to large database to acquire useful information from a great deal data is developed. Data mining (DM) or knowledge discover from database (KDD) is to discover useful information and potential knowledge from plentiful and incomplete and fuzzy and random data which are hid and are not known by people. These discovered knowledge may be used to manage information and optimize queries and make decision and control procedure and maintain database and so on. So data mining is a very valuable new database research area, and it is a crossed subject that adopts theory and technology of database and artificial intelligence and machine learning and statistics and so on. Classifying based on Bayes Technology has got more and more interests in the field of data mining. The main work of the thesis: 1.The fundamental technologies of data mining and classification are introduced. Several typical classification algorithms are compared including decision-tree and neural network algorithm and Bayesian algorithm. 2.The main theories of Bayesian classification are discussed, including Bayes Theorem ,Na?ve Bayesian classification and Bayesian Networks classification. 3.The learning of Bayesian Networks is studied, including structure learning of Bayesian Networks and parameter learning of Bayesian Networks. 4.The system of data mining is introduced and the module of Bayesian algorithm is mainly introduced.