Data Mining Techniques And Its Application In The Consumer's Behavior Analysis System

Data mining technique is one of the hot issues in the development of computer technology in recent years. We can get hidden rules and patterns that give support to the decision by means of the effective data mining on a great number of history data. These rules and patterns, which commonly can not be found out by simple querying on the data nor be found in limited time, can be transferred into knowledge under the professionals' recognition. Data mining always faces complicated tasks that including classification, prediction, association rule discovering and clustering, etc.In the first part of this paper, we give brief introduction to data mining and its relation with data warehouse and OLAP(Online Analytical Processing) technology. Then, we give a solution of building a consumers' behavior data-mart using the Microsoft SQL Server 2000 data warehouse and OLAP technology, considering the consumers' behavior analysis system's demands and the existed data. This data-mart provides clean data to the data mining software. We then introduce the technique of how to develop the client program using ADO/MD component to visualization data. At last, we put much efforts on introducing our two data mining tasks we have completed on the consumers'behavior data——mining association rules based on the Apriori algorithm andprediction the class of the registered consumers by means of build decision tree based on the SLIQ algorithm.