Decision Support Research And Application Of Data Warehousing And Data Mining System

With the development of society, the amount of information has become larger and larger. Consequently, the importance of information processing is increasing. In the 1980's, DW (Data Warehouse), which is very widely related with DSS(Decision Support System), is introduced into the IT industry world. DW adopts a new way to organize data and to make analysis to support decision. This thesis mainly studies the application of the theory of DW and DM in the public transportation DSS. My paper also puts forward a scheme of DSS based on data warehouse and proves the necessity and feasibility of this scheme. Then we study KDD (Knowledge Discovery in Databases), which is heavily relevant to artificial intelligence and database system. In today's digital society, the explosive growth of many business, and scientific databases have far outpaced our ability to interpret and digest this data, creating a need for a new and generation of tools and techniques for automated and intelligent database analysis, and that is the goal of KDD. We focus on the following several aspects in KDD, the processing models of KDD, classification discovery which is one of the most important and widely used tasks in KDD, decision-tree algorithm is one of the classification methods. We discuss several kinds of classification methods and the implementation of the 1D3 algorithm in detail. C 1 assification is one of the most important tasks of Data Mining. The goal of classification is to learn a classifying model which could be used for prediction. The thesis studies the classification methods, give out the detail implementation of one classification method: decision-tree algorithm. Finally, we apply classification method in instance citizen outing data and draw some rules for the prediction on the future bus passages.