Efficient discovery of frequent itemsets in large datasets is a key component of many data mining tasks. In-core algorithms---which operate entirely in main memory and avoid expensive disk accesses---and in particular the prefix tree-based algorithm FP-growth are generally among the most efficient of the available algorithms. Unfortunately, their excessive memory requirements render them inapplicable for large datasets with many distinct items and/or itemsets of high cardinality. To overcome this limitation, we propose two novel data structures---the CFP-tree and the CFP-array---, which reduce memory consumption by about an order of magnitude. This allows us to process significantly larger datasets in main memory than previously possible. Our data structures are based on structural modifications of the prefix tree that increase compressability, an optimized physical representation, lightweight compression techniques, and intelligent node ordering and indexing. Experiments with both real-world and synthetic datasets show the effectiveness of our approach.