In this paper, we present a novel algorithm, called FMiner, to mine the complete set of frequent itemsets by pattern growth. The F-Miner algorithm uses two new compact data structures, Ascending FP-Tree (AFP-Tree) and Frequent Pattern Forest (FP-Forest), to represent the conditional databases. When we construct an AFP-Tree, the items in frequent 1-itemset are ordered in frequency ascending order. The AFP-Tree structure is traversed in top-down depth-first order. The root of the AFP-Tree is not "null", but an item which can identify this tree. AFP-Tree has a onedimensional array which stores the counts of every treenode?s item except root-node. In F-Miner, we need many AFP-Trees to store a conditional database; these trees construct one forest, called FP-Forest. We test our algorithm versus several other algorithms on real world datasets, such as BMS-POS. The experimental results show that our algorithm is an efficient algorithm on both sparse and dense databases.