Research Of Algorithem Of Mining Association Rule Based On Data Warehouse

Association rule is one of active part of data mining. It has been used to find potentialand interested information for customers from databases or data warehouses. On the basis ofanalyzing the defect of existing association rule algorithms, we propose a series of novel ideas,realize several advanced algorithms and achieve favorable result. The main research worksare follows:(1) The concept of the best support and the best confident are put forward in associationrule. It can be used to mine the most useful association rules in the certain circumstance andattain favorable result of making decision.(2) An advanced algorithm of mining parallel association rules is proposed. By gettingrid of the times of scanning databases or data warehouses and the number of candidate items,we can enhance the efficiency of the algorithm. Moreover, the extensity of new algorithm isbetter than others.(3) A parallel algorithm for mining weighted association rules is proposed. In order tomake the algorithm correspond with the reality, we offer each item a different weight value sothat it can represent the importance of individual items from databases or data warehouses. Inthis way, we may discover the useful association rules for customs.(4) An algorithm of mining quantitative association rules is put forward. Quantitativeattribute values are partitioned into basic intervals according to their distribution in thedatabases or data warehouses, and if possible, the adjacent basic intervals will be merged.Then the intervals are mapped into Boolean attributes. At last, general interesting quantitativeassociation rules can be mined.Theory analysis and simulation results for data show the result based on these methodsare improved much more than normal algorithm's.