| Data Mining has become one of the most popular researches in IT industry in recent years, but the research is mainly concentrated on algorithm. Presently the application of DM is only confined to domain experts, since almost all the Data Mining systems are not seamlessly integrated with Relational Database, and the deficiency of Data Mining language is also the reason for it. Therefore the integration of Data Mining with database as well as the development of Data Mining standard language has become one hot spot in current researches in DM field.Under such a background, the thesis, in combination of a Sino-French cooperation project "The Application of Data Mining in GIS", which was financed by Shanghai Education Commission Section (2001) 71 as Key Discipline of Shanghai Education Commission, studies the formulation of Data Mining solution based on SQL Server as well as the application of DM technology in GIS.SQL Server and Data Mining constitute the main line throughout the whole thesis. The author's object is, on the basis of OLE DB For DM, to integrate Data Mining with Relational Database as well as the application program. For this reason the thesis particularly discusses three ways of building the solution for Data Mining based on SQL Server.The first way is to use the Data Mining algorithms provided by SQL Server Analysis Services to solve the problems of Data Mining. Those algorithms are completed in accordance with OLE DB For DM, so that they can be directly used to build Data Mining models from Relational Database. The models will be stored in PMML style and can be used in any application program. In this part, the author provides the system structure and gives an example of this kind of Data Mining solutions.The second way of building the solution for Data Mining based on SQL Server is to embed some Data Mining algorithms designed by the author himself or others into SQL Server as independent program modules. Then these modules could be used to find knowledge in data warehouse.The third way is to use the interfaces provided by SQL Server AnalysisServices to integrate the Data Mining algorithms of third providers. The algorithms must accord with OLE DB For DM, so that they can communicate with Analysis Services through a series of COM interfaces. In this part, the author develops a DLL based on line regression algorithm in VC++ development environment. The algorithm will be useful after compiled the DLL, now people can use this algorithm to build Data Mining model and train it in Relational Database.Finally, the author researches how to integrate Data Mining into GIS and builds two solutions of Data Mining in GIS based on SQL Server. In the first solution, a new improved integrated clustering analysis algorithm is used to carry out course design. In the second solution, the author separately builds Data Mining models from server and client in GIS application program. In this way, the author also designs the passage with Microsoft clustering algorithm and obtains some other valuable results.Besides, the author makes comparison between and among the three solutions of Data Mining based on SQL Server discussed in this thesis, and analyzes two kinds of results with different methods to optimize passage design.This thesis facilitates the formulation of Data Mining solutions based on SQL Server, and bridges the gap between Data Mining and Relational Database. In this way, Data Mining application program in Relational Database or Data Warehouse can be directly developed or operated. In addition, the way to formulate Data Mining solutions in GIS is showed in the thesis as well.Hao Ruiji (Control Theory and Control Engineering) Directed by Prof. Tang Tianhao and Prof. Shi Weifeng... |
