Research And Application Of Algorithem Of Mining Association Rule Based On Data Warehouse

In the past several ten years, both commercial enterprises and scientific research institutions or government departments, the MIS system has been widely used in information management. The MIS system which is based on affair processing accumulates a massive and complicated data. Meanwhile it is also convenient for data management. The contradiction between the explosive growth of data and comparative lack of knowledge makes data mining become one of the foremost frontier directions of research in regions of international information-making and artificial intelligence. Among the research, the exploration indicates that association rule is an important direction of data mining.This thesis has deeply analysis and research on the classical association rule of algorithm and provides some improvements to the demerits of original algorithm. Fortunately it achieved some certain effect, the research includes:(1) Changing the classical algorithm for one-way search methods, and use a top-down and bottom-up two-way search strategy. Both items that are set for the number or size of the smallest support, can quickly find Frequent Item sets, experiments improved that it can greatly increases the efficiency of algorithms.(2) Using the strategy of bottom-up to generate infrequent item sets for dimension reduction of top-down operation can greatly reduced the number of frequent top-down candidates set.(3) Using the structure of the matrix to preserve database can reduce computer I/O operation, using the database storage and compress function to reduce the data, will improve the efficiency of data traversing.(4) Research the data mining of quantitative association rule, using clustering algorithm, to classify the space and then mapped those spaces to Boolean attribution. After that, the research discovers association rule which is interested by the user.Eventually, combine the theory with practice, use the Data Warehouse of student's score as the source of the data, mining the association rules, meanwhile use OLAP(Online Analytical Processing) and other facilities to show that in many dimensions, then realizing the visualization of the data analysis.