Based On Data Warehouse And OLAP Decision-Making Technology

With wide applications of computers and automated data collection tools, massive amounts of data have been continuously collected and stored in Databases, which creates an imminent need and great opportunities for mining interesting knowledge from data. Association rule mining is one kind of data mining techniques which discovers strong association or correlation relationships among data .The discovered rules may help market basket or cross-sales analysis, decision makingand business management.Apriori algorithm is a classis algorithm of association rules mining, But along with the scale increasing of the database, it have disadvantages. Apriori gets candidate itemsets, and then delete un-frequent itemsets. Take count of every candidate itemset will use much CPU time.In this thesis, we have a concern association rule mining method, that is, based on on-line analytical processing (OLAP) technology and association rule mining algorithm has been widely applied to the current on-line analytical processing (OLAP) technologies and association rule mining algorithm Apriori together. This algorithm has been in the supermarket intelligence decision- making software analysis tools to achieve DB-Miner system. Our research shows that the OLAP based association rule mining algorithm comparison of the original algorithm in the stability and efficiency of the operation has excellent.