Abstract
Frequent Itemsets (FI) Mining is one of the most researched areas of data mining. When some new transactions are appended, deleted or modified in a dataset, updating FI is a nontrivial task since such updates may invalidate existing FI or introduce new ones. In this paper a novel algorithm suitable for FI mining in dynamic datasets named Incremental Compressed Arrays is presented. In the experiments, our algorithm was compared against some algorithms as Eclat, PatriciaMine and FP-growth when new transactions are added or deleted.
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Hernández-León, R., Hernández-Palancar, J., Carrasco-Ochoa, J.A., Martínez-Trinidad, J.F. (2008). A Novel Incremental Algorithm for Frequent Itemsets Mining in Dynamic Datasets. In: Ruiz-Shulcloper, J., Kropatsch, W.G. (eds) Progress in Pattern Recognition, Image Analysis and Applications. CIARP 2008. Lecture Notes in Computer Science, vol 5197. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85920-8_18
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DOI: https://doi.org/10.1007/978-3-540-85920-8_18
Publisher Name: Springer, Berlin, Heidelberg
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