Due to the improvement of information technologies and popularization of computers, collecting information becomes easier, rapider and more convenient than before. As the time goes by, database accumulates huge and knowledge-hiding information. Therefore, how to correctly uncover and efficiently mining hidden knowledge from those information becomes a very important issue. Hence the technology of data mining becomes one of the solutions. Among the data mining technologies association rules mining is one of the most popular technologies to be used. Association rules mining explores the approaches to extract the frequent itemsets from large database and to derive the knowledge behind implicitly. The Apriori algorithm is one of the most frequently used algorithms. Although the Apriori algorithm can successful derive the association rules from database, the Apriori algorithm has two major defects: First, the Apriori algorithm produces large amounts of candidate itemsets during extracting the frequent itemsets from large database. Secondly, the whole database is scanned many times which leads to inefficient performance. Many researches try to improve the performance of the Apriori algorithm, but still not escape from the frame of the Apriori algorithm and lead to a little improvement of the performance. In this paper we propose ICI (Incremental Combination Itemsets) which escapes the frame of Apriori algorithm, and it only needs to scan whole database once during extracting the frequent itemsets from large database. Therefore, the ICI algorithm efficiently reduces the I/O time, and rapidly extracts the frequent itemsets from large database, and makes data mining more efficient than before. Meanwhile, ICI algorithm doesn’t need to scan database and reconstruct data structure again when database is updated or minimum support is varied. Therefore, it can be applied to online incremental mining applications without any modification.