From Path Tree To Frequent Patterns: A Framework for Mining Frequent Patterns

In this paper, we propose a new framework for mining frequent patterns from large transactional databases. The core of the framework is of a novel coded prefix-path tree with two representations, namely, a memory-based prefixpath tree and a disk-based prefix-path tree. The disk-based prefix-path tree is simple in its data structure yet rich in information contained, and is small in size. The memorybased prefix-path tree is simple and compact. Upon the memory-based prefix-path tree, a new depth-first frequent pattern discovery algorithm, called -Mine, is proposed in this paper that outperforms FP-growth significantly. The memory-based prefix-path tree can be stored on disk using a disk-based prefix-path tree with assistance of the new coding scheme. We present efficient loading algorithms to load the minimal required disk-based prefix-path tree into main memory. Our technique is to push constraints into the loading process, which has not been well studied yet.