|Table of Contents|

Fast algorithm of frequent itemset mining based on matrix from uncertain data

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

Issue:
2015年04期
Page:
420-
Research Field:
Publishing date:

Info

Title:
Fast algorithm of frequent itemset mining based on matrix from uncertain data
Author(s):
Liu Zhiyi1Chang Rui2
1.Department of Computer Science and Information Engineering; 2.Department of Project Finance,Changzhou Institute of Technology,Changzhou 213002,China
Keywords:
uncertain databases frequent itemsets expected support fast mining
PACS:
TP311
DOI:
-
Abstract:
The CUF-growth algorithm gives an upper bound on the expected support of itemsets,but the estimate is too high.It has own bottleneck that needs to build conditional CUF-tree recursively in the mining process for getting candidate itemsets.According to the deficiency of the CUF-growth,the UFIM-Matrix(Uncertain frequent itemset mining-matrix)algorithm is proposed.This algorithm does not need to build a pattern tree while it generates smaller candidate sets by using a matrix structure and an improved method to calculate the upper bound of the expected support of itemsets.It can greatly reduce the cost of computing and improve the mining efficiency.The experimental results indicate the algorithm is more effective and efficient.

References:

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Memo:
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Last Update: 2015-08-31