Identifying Approximate Itemsets of Interest in Large Databases

This paper presents a method for discovering approximate frequent itemsets of interest in large scale databases. This method uses the central limit theorem to increase efficiency, enabling us to reduce the sample size by about half compared to previous approximations. Further efficiency is gained by...

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Bibliographic Details
Published inApplied intelligence (Dordrecht, Netherlands) Vol. 18; no. 1; pp. 91 - 104
Main Authors Zhang, Chengqi, Zhang, Shichao, Webb, Geoffrey I
Format Journal Article
LanguageEnglish
Published Boston Springer Nature B.V 01.01.2003
Subjects
Algorithms
Approximation
Data mining
Efficiency
Intelligence
Mathematical models
Online databases
Pruning
Searching
Studies
Theorems
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Summary:This paper presents a method for discovering approximate frequent itemsets of interest in large scale databases. This method uses the central limit theorem to increase efficiency, enabling us to reduce the sample size by about half compared to previous approximations. Further efficiency is gained by pruning from the search space uninteresting frequent itemsets. In addition to improving efficiency, this measure also reduces the number of itemsets that the user need consider. The model and algorithm have been implemented and evaluated using both synthetic and real-world databases. Our experimental results demonstrate the efficiency of the approach.[PUBLICATION ABSTRACT]
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ISSN:0924-669X
1573-7497
DOI:10.1023/A:1020995206763