Model Design, Based On The Apriori Algorithm And Olap Association Rules Mining

When enters for the 21st century, Many Enterprise and another organizations have gradually finished to management by MIS (Management information system) and another information Systems, and these organizations accumulation massive amounts of data. How to obtain useful and hidden knowledge from these huge numbers of data has already become important part of information technology. Data mining aims at the task of the above work. Association rules mining is a form of data mining to discover previously unknown, interesting relationships among attributes from large databases. Market-basket analysis is a classic application of association rules mining. It analyses the customers' habits by find the relationships between the purchases they put in theirs marker-baskets. It can tell which products were often purchased together. The result can help arrange the commodity layout and establish sales strategy etc.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.OLAP is a fast query and analyze technology of sharable multidimensional data. This paper analyses the characteristics of the Apriori algorithm and then proposes a design method of OLAP mining model accord to the algorithm. The design of the dimensions and measures in data cube fully consider the characteristics of the Apriori algorithm to make the data cube materialize more middle data the algorithm needed. Then adapt the algorithm to the data cube. Study shows that this method presents well on flexibility and efficiency.