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- W2005183791 abstract "It is difficult to discover frequent itemsets based on whole data from data stream since data stream has the characteristics of infinity and continuity. Therefore, a specialized data mining method, which reflects the properties of data and the requirement of users, is required. In this paper, we propose the method of FIMWB discovering the frequent itemsets which are reflecting the property that the recent events are more important than old events. Data stream is splitted into batches according to the given time interval. Our method gives a weighted value to each batch. It reflects user`s interestedness for recent events. FP-Digraph discovers the frequent itemsets by using the result of FIMWB. Experimental result shows that FIMWB can reduce the generation of useless items and FP-Digraph method shows that it is suitable for real-time environment in comparison to a method based on a tree(FP-Tree)." @default.
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- W2005183791 date "2011-01-28" @default.
- W2005183791 modified "2023-10-18" @default.
- W2005183791 title "Discovering Frequent Itemsets Reflected User Characteristics Using Weighted Batch based on Data Stream" @default.
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- W2005183791 doi "https://doi.org/10.5392/jkca.2011.11.1.056" @default.
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