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http://dx.doi.org/10.5392/JKCA.2011.11.1.056

Discovering Frequent Itemsets Reflected User Characteristics Using Weighted Batch based on Data Stream  

Seo, Bok-Il (전남대학교 전자컴퓨터공학부)
Kim, Jae-In (전남대학교 전자컴퓨터공학부)
Hwang, Bu-Hyun (전남대학교 전자컴퓨터공학부)
Publication Information
The Journal of the Korea Contents Association / v.11, no.1, 2011 , pp. 56-64 More about this Journal
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).
Keywords
Data Stream; Weighted Value; Frequent Itemsets; Interval; Batch;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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