Abstract
In the last years, the problem of Frequent Itemset Mining (FIM) from imperfect databases has been sufficiently tackled to handle many kinds of data imperfection. However, frequent itemsets discovered from databases describe only the current state of the data. In other words, when data are updated, the frequent itemsets could no longer reflect the data, i.e., the data updates could invalidate some frequent itemsets and vice versa, some infrequent ones could become valid. In this paper, we try to resolve the problem of Incremental Maintenance of Frequent Itemsets (IMFI) in the context of evidential data. We introduce a new maintenance method whose experimentations show efficiency compared to classic methods.
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Bach Tobji, M.A., Ben Yaghlane, B., Mellouli, K. (2009). Incremental Maintenance of Frequent Itemsets in Evidential Databases. In: Sossai, C., Chemello, G. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2009. Lecture Notes in Computer Science(), vol 5590. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02906-6_40
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DOI: https://doi.org/10.1007/978-3-642-02906-6_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02905-9
Online ISBN: 978-3-642-02906-6
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