Abstract
We present a new method for incremental discovery of association rules, which is highly general and independent of a mining algorithm. The heart of the method is the rule maintenance algorithm, which keeps the base of discovered rules as if they were mined in a single run through the whole transaction database. For more general and flexible results we take into account thresholds of rules statistical significance and influence of time. The method can be used as a learning model in knowledge-based systems with bounded resources, e.g. software agents.
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Dudek, D., Zgrzywa, A. (2005). The Incremental Method for Discovery of Association Rules. In: Kurzyński, M., Puchała, E., Woźniak, M., żołnierek, A. (eds) Computer Recognition Systems. Advances in Soft Computing, vol 30. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32390-2_16
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DOI: https://doi.org/10.1007/3-540-32390-2_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25054-8
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