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- W2246221833 abstract "An association rule mining is often performed with a dynamic database and hierarchical items. The big problem of data mining process is a maintenance association rules while the database always changing. The purpose of this study is to extend the MLUp algorithm which can maintain a multilevel association rules discovery at the same minimum support threshold. In general, several mining tasks are required to deal with different support thresholds. MLUpCS can deal with a maintaining of mining multilevel association rules in dynamic databases under the different support threshold without re-mine a whole database. The result of MLUpCS algorithm experiment has shown how better performance than ML-T2 algorithm. The experimental results show the superior performance of MLUpCS when compared with ML-T2." @default.
- W2246221833 created "2016-06-24" @default.
- W2246221833 creator A5055734408 @default.
- W2246221833 creator A5059466448 @default.
- W2246221833 date "2015-08-01" @default.
- W2246221833 modified "2023-09-25" @default.
- W2246221833 title "Maintenance of multi-level association rules discovery in dynamic database under a change of support threshold" @default.
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- W2246221833 doi "https://doi.org/10.1109/fskd.2015.7382014" @default.
- W2246221833 hasPublicationYear "2015" @default.
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