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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 2/2017

17.02.2015 | Original Article

TDUP: an approach to incremental mining of frequent itemsets with three-way-decision pattern updating

verfasst von: Yao Li, Zhi-Heng Zhang, Wen-Bin Chen, Fan Min

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 2/2017

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Abstract

Finding an efficient approach to incrementally update and maintain frequent itemsets is an important aspect of data mining. Earlier incremental algorithms focused on reducing the number of scans of the original database while it is updated. However, they still required the database to be rescanned in some situations. Here we propose a three-way decision update pattern approach (TDUP) along with a synchronization mechanism for this issue. With two support-based measures, all possible itemsets are divided into positive, boundary, and negative regions. TDUP efficiently updates frequent itemsets online, while the synchronization mechanism is periodically triggered to recompute the itemsets offline. The operation of the mechanism based on appropriate settings of two support-based measures is examined through experiments. Results from three real-world data sets show that the proposed approach is efficient and reliable.
Vorheriger Artikel Semi-supervised clustering for gene-expression data in multiobjective optimization framework
Nächster Artikel Efficiently detecting overlapping communities using seeding and semi-supervised learning

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Metadaten
Titel
TDUP: an approach to incremental mining of frequent itemsets with three-way-decision pattern updating
verfasst von
Yao Li
Zhi-Heng Zhang
Wen-Bin Chen
Fan Min
Publikationsdatum
17.02.2015
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 2/2017
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-015-0337-6

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