Application And Study Of Data Warehouse,Data Mining Base On Insurance

Data Warehouse ( DW ) is a new and advanced database technology, it has been developed very quickly in recent years. DW uses an effective way to operate a lot of data stored in the database or other files and transform them into useful information, then the decision-maker can have a great help in the decision-making process by analyzing these transformed information. Data Mining ( DM) is based on database or data warehousing. It can discover hidden patterns, interesting patterns and high information using decision tree, association rules, clustering, neural networks.As the business developed, the PICC faces a lot of historical data and transaction data. How to sufficiently use these plentiful data to provide critical information for the PICC's governor, better survey for the business, has become an important thing. In order to solve this problem, I have proposed that construct the data warehouse platform, found the contained information to carry on information analysis and decision support.This paper firstly discussed the conception, modeling and architecture of the Database Warehouse, the design of physical database as well as the Database Mining Technology and the On-line Analytical Processing. Through the study on above-mentioned technology, I provided the data warehouse system for the Hebei People's Insurance Company of China. It included the data warehouse model design, data integration, the application of data mining algorithms. The warehouse model of data was discussed from three aspects which were conceptual model, logic model and physical model. Because the quality of the data was " bottleneck" while implementing in the data warehouse, this paper has introduced the problem and solution found in the data integrate. In the application of data mining algorithms, I used the decision tree to solve the question of the customer behavior classifies and used association rule to solve the cross-selling question. As to solving related regular increments upgrade problem, stored the assembling itemset of the original data set and the new data set in the middle table. Then used minimum support to cut on the itemset, found the frequent set. This method needn't be collected and search for the original data again, improved the execution efficiency of the algorithm...