Memory Efficient Tries for Sequential Pattern Mining

The rapid and continuous growth of data has increased the need for scalable mining algorithms in unsupervised learning and knowledge discovery. In this paper, we focus on Sequential Pattern Mining (SPM), a fundamental topic in knowledge discovery that faces a well-known memory bottleneck. We examine generic dataset modeling techniques and show how they can be used to improve SPM algorithms in time and memory usage. In particular, we develop trie-based dataset models and associated mining algorithms that can represent as well as effectively mine orders of magnitude larger datasets compared to the state of the art. Numerical results on real-life large-size test instances show that our algorithms are also faster and more memory efficient in practice.