Abstract
The steady growth in the size of data has encouraged the emergence of advanced main memory trie-based data structures. Concurrently, more acute knowledge extraction techniques are devised for the discovery of compact and lossless knowledge formally expressed by generic bases. In this paper, we present an approach for deriving generic bases of association rules. Using this approach, we construct small partially ordered sub-structures. Then, these ordered sub-structures are parsed to derive, in a straightforward manner, local generic association bases. Finally, local bases are merged to generate the global one. Extensive experiments carried out essentially showed that the proposed data structure allows to generate a more compact representation of an extraction context comparatively to existing approaches in literature.
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© 2005 Springer-Verlag Berlin Heidelberg
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Yahia, S.B., Slimani, Y., Rezgui, J. (2005). A Divide and Conquer Approach for Deriving Partially Ordered Sub-structures. In: Ho, T.B., Cheung, D., Liu, H. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2005. Lecture Notes in Computer Science(), vol 3518. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11430919_12
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DOI: https://doi.org/10.1007/11430919_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26076-9
Online ISBN: 978-3-540-31935-1
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