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- W2357782521 abstract "To overcome the limitation of existing model on evolving data and long cycle, this paper proposes a new method which can discover long cycle frequent association rules based on evolving data. Under the dynamic circumstance of evolving data ,the cyclic segment can be obtained through dynamic clustering analysis. In each segement, some algorithms for mining association rules with low support are used to discover the long cycle association rules. The whole algorithm can be maintained dynamically on the basis of GEMM algorithm ." @default.
- W2357782521 created "2016-06-24" @default.
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- W2357782521 date "2002-01-01" @default.
- W2357782521 modified "2023-09-27" @default.
- W2357782521 title "Mining Long Cycle Frequent Association Rules Based on Evolving Data" @default.
- W2357782521 hasPublicationYear "2002" @default.
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