Approximation of Representative Frequent Itemsets Mining in Uncertain Data
CHEN Fengjuan
Abstract：Since mining frequent itemsets in uncertain data is the fundamental step of many data mining tasks, it has attracted much attention from lots of researchers.However, this work will find large amount of frequent itemsets when the dataset is huge.It puts an obstacle to the next work.To address this problem, an efficient approximation mining algorithm of representative frequent itemsets is proposed in this paper.In the method, the VC-dimension theory is used to reduce the size of sample and provide satisfactory performance guarantees on the quality of the approximation.The algorithm is based on random sampling to mine representative frequent itemsets.It improves efficiency of mining task and reduces the number of frequent itemsets.
Keywords：uncertain datarepresentative frequent itemsetapproximation algorithmVC-dimension
Publication Date：2017-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 266-271 )