Abstract
Multi-dimensional sequential pattern mining attempts to find much more informative frequent patterns suitable for immediate use. In this paper, a novel data model called multi-dimensional concept lattice is proposed and, based on which, a new 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 of the proposed approach is evaluated on both synthetic and real-life financial date sets.
This work was sponsored by Natural Science Foundation of China (NSFC) under Grant No. 60373099.
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Jin, Y., Zuo, W. (2006). Multi-dimensional Sequential Pattern Mining Based on Concept Lattice. In: Li, X., Zaïane, O.R., Li, Z. (eds) Advanced Data Mining and Applications. ADMA 2006. Lecture Notes in Computer Science(), vol 4093. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11811305_77
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DOI: https://doi.org/10.1007/11811305_77
Publisher Name: Springer, Berlin, Heidelberg
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