Abstract
In this paper, a novel Concept Lattice-based Incrementally Large Itemset Generation Algorithm (CLLGA) is presented to discover association rules. As an important database discovery method, the kernel of association mining is the acquisition of large itemsets. According to Hu’s [5] algorithm to generate market-basket association rules, an improved concept lattice based approach for incrementally acquiring large itemsets is introduced. The approach is especially efficient when the database is dynamically updated (insertion or deletion). By means of attaching frequency information to each itemset, i.e., each node in the lattice, the corresponding support and confidence measure can be obtained without constructing the complete lattice. Therefore, association rules can be derived from the concept lattice. Compare with Hu’s approach, the time complexity of our algorithm can be reduced greatly.
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© 2001 Physica-Verlag Heidelberg
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Zhao, Y., Ruan, D., Shi, P. (2001). Concept Lattice-based Approach for Incrementally Association Mining. In: Ruan, D., Kacprzyk, J., Fedrizzi, M. (eds) Soft Computing for Risk Evaluation and Management. Studies in Fuzziness and Soft Computing, vol 76. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1814-7_8
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DOI: https://doi.org/10.1007/978-3-7908-1814-7_8
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-662-00348-0
Online ISBN: 978-3-7908-1814-7
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