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http://dx.doi.org/10.3745/KIPSTD.2008.15-D.4.475

Approximation of Frequent Itemsets with Maximum Size by One-scan for Association Rule Mining Application  

Han, Gab-Soo (두원공과대학 컴퓨터정보과)
Publication Information
The KIPS Transactions:PartD / v.15D, no.4, 2008 , pp. 475-484 More about this Journal
Abstract
Nowadays, lots of data mining applications based on continuous and online real time are increasing by the rapid growth of the data processing technique. In order to do association rule mining in that application, we have to use new techniques to find the frequent itemsets. Most of the existing techniques to find the frequent itemsets should scan the total database repeatedly. But in the application based on the continuous and online real time, it is impossible to scan the total database repeatedly. We have to find the frequent itemsets with only one scan of the data interval for that kind of application. So in this paper we propose an approximation technique which finds the maximum size of the frequent itemsets and items included in the maximum size of the frequent itemsets for the processing of association rule mining.
Keywords
Frequent Itemsets; Association Rule Mining; Data Interval;
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