Multi-dimensional sequential pattern mining is the process of mining association rules from one or more dimensions of background information in which the order of the dimension values is not relevant, alongside mining sequential patterns from one or more dimensions of information in which the order is important. Multi-dimensional sequential patterns are much more informative frequent patterns which are suitable for immediate use. Although some work has been conducted for mining multi-dimensional sequential patterns, association patterns and sequential patterns are mined separately based on different data structures. In this paper, a novel data model called multi-dimensional concept lattice is proposed, which is the extension or generalization toward concept lattice. The intension of multi-dimensional concept lattice is more informative, which is constituted of one or more ordered task-relevant dimensions and one or more unordered background dimensions. Moreover, an incremental multi-dimensional sequential pattern mining algorithm is developed. The proposed algorithm integrates sequential pattern mining and association pattern mining with a uniform data structure and makes the mining process more efficient. The performance study on synthetic datasets shows the scalability and effectiveness of the proposed algorithm. At the same time, the application on the real-life financial datasets demonstrates the practicability of the approach.