System Based On Data Mining For The Customs To Execute The Law And Evaluate

The rapid development in and wide application of computer network and database technology makes acute the "data-rich but knowledge-poor" problem. So,we must find new ways to extract potentially useful information and knowledge from data. Data mining and KDD aim to provide neoteric techniques and tools to automatically analyze the data,to automatically classify it,to automatically summarize it,to automatically discover and characterize trends in it,and to automatically flag anomalies.Data mining is a promising and flourishing frontier in database systems and new database applications. Knowledge discovery is a uncommon process of identifying effective new potentially useful and finally accessible patterns. Data mining is an essential step in the process of KDD. KDD is a multi-disciplinary field of research. Statistics,database technology,computer science,mode recognition,artificial intelligence and machine learning all make a contribution.Data mining has been widely used in biomedical field financial field retail industry and telecommunication industry. This paper discusses application of data mining in government,and gives an actual example. After comparing all kinds of tools for data mining provided by SAS,we select methods adapt to the task,and all these algorithm is applied in the project of General Custom for customs directly under General Custom to execute the law and evaluate work. The result is satisfied.There are eight chapters in this paper.Chapter 1:The concept of and development in knowledge discovery and data mining,the process of knowledge discovery,the classification of data .mining,the study background and significance of the task,the main contents of this paper are introduced in this chapter.Chapter 2:This chapter describes techniques for preprocessing the data priorto mining. Methods of data cleaning,data integration and transformation,and data reduction are discussed.Chapter 3:This chapter describes methods for data prediction based on regression. Linear and Multiple Regression,Curvilinear Regression and Stepwise Regression are included.Chapter 4:This chapter describes methods of cluster analysis. It first introduces the concept of data clustering and data classification. Then it discusses eleven methods of cluster analysis.Chapter 5:This chapter describes methods of decision tree. The arithmetic for constructing a decision tree is described in detail. Some improvements are presented.Chapter 6:This chapter describes techniques of neural network. It mainly discusses artificial nerve cell and perceptron. BP network and RBF network are described in this chapter.Chapter 7:This chapter introduces the setting and methods of the system,the function requirement,the detailed design and the final implements.Chapter 8:The further research directions of data mining are presented in this chapter.