A SAT model to mine flexible sequences in transactional datasets
This work addresses the problem of inflexibility in pattern mining for researchers and practitioners in data mining, offering a novel method that is incremental in nature.
The paper tackled the lack of flexibility in traditional pattern mining algorithms by proposing a SAT formulation to mine frequent flexible sequences in transactional datasets, demonstrating practical feasibility through experiments on two real datasets.
Traditional pattern mining algorithms generally suffer from a lack of flexibility. In this paper, we propose a SAT formulation of the problem to successfully mine frequent flexible sequences occurring in transactional datasets. Our SAT-based approach can easily be extended with extra constraints to address a broad range of pattern mining applications. To demonstrate this claim, we formulate and add several constraints, such as gap and span constraints, to our model in order to extract more specific patterns. We also use interactive solving to perform important derived tasks, such as closed pattern mining or maximal pattern mining. Finally, we prove the practical feasibility of our SAT model by running experiments on two real datasets.