Recently Customer Relationship Management is one of the hottest issues in cooperation. In order to properly arrange the positions of products, Cooperation need to understand customers' shopping behaviors and the associations between products. In this way, we can increase the customers' satisfactions and decrease the searching time during shopping. Besides, we can increase the quantity of purchase products and the profits. Thus, it is very important to use the technology of data mining to find the useful association rules and to provide the cooperation's decision supports. In this paper we propose a new algorithm QPD (Quick Patterns Decomposition) to find the association rules from large transaction databases. The merits of QPD algorithm are: 1. In data mining process it only needs to scan whole transaction database once. 2. Using Patterns method to increase the performance of data mining process. 3. Using mask and Boolean method to decompose the itemsets to sub-itemsets. 4. When minimum support changed we do not need to process mining process again. 5. It does not need to rescan the original database for mining the association rules from the incrementally growing databases. From above illustration we know that by using QPD algorithm to process association analysis has better performance than Apriori-based algorithms. In association rule's reasoning process, it won't produce the unnecessary candidate items. Therefore it can fast obtain the information correctly and effectively and reduce the time cost, and fastly reflect the market demand. That will greatly promote the competitive advantage.