Progressive sampling for association rules based on samplings error estimation

Kun Ta Chuang, Ming Syan Chen, Wen Chieh Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

21 Citations (Scopus)

Abstract

We explore in this paper a progressive sampling algorithm, called Sampling Error Estimation (SEE), which aims to identify an appropriate sample size for mining association rules. SEE has two advantages over previous works in the literature. First, SEE is highly efficient because an appropriate sample size can be determined without the need of executing association rules. Second, the identified sample size of SEE is very accurate, meaning that association rules can be highly efficiently executed on a sample of this size to obtain a sufficiently accurate result. This is attributed to the merit of SEE for being able to significantly reduce the influence of randomness by examining several samples with the same size in one database scan. As validated by experiments on various real data and synthetic data, SEE can achieve very prominent improvement in efficiency and also the resulting accuracy over previous works.

Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 9th Pacific-Asia Conference, PAKDD 2005, Proceedings
PublisherSpringer Verlag
Pages505-515
Number of pages11
ISBN (Print)3540260765, 9783540260769
DOIs
Publication statusPublished - 2005 Jan 1
Event9th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2005 - Hanoi, Viet Nam
Duration: 2005 May 182005 May 20

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3518 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other9th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2005
CountryViet Nam
CityHanoi
Period05-05-1805-05-20

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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