Abstract
This paper proposes a new method for generating multi-dimensional sequential patterns. While the current sequential pattern methods are generating patterns within a single attribute, the proposed method is able to detect them among different attributes. We employ an information theoretic method for generating multi-dimensional sequential patterns with the use of Hellinger entropy measure. A number of theorems are proposed to reduce the computational complexity of the sequential pattern systems. The proposed method is tested on some synthesized transaction databases.
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© 2005 Springer-Verlag Berlin Heidelberg
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Lee, CH. (2005). An Entropy-Based Approach for Generating Multi-dimensional Sequential Patterns. In: Jorge, A.M., Torgo, L., Brazdil, P., Camacho, R., Gama, J. (eds) Knowledge Discovery in Databases: PKDD 2005. PKDD 2005. Lecture Notes in Computer Science(), vol 3721. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11564126_61
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DOI: https://doi.org/10.1007/11564126_61
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29244-9
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