Alternative TitleQuantitative Model and Fast Estimation Algorithm of Sampling Error for Association Rule Mining
贾彩燕1; 陆汝钤2
Source Publication计算机学报
Other AbstractSampling is a simple and effective technique to improve the efficiency and the scalability of algorithms for association rule mining. However, there is lack of necessary research to define the degree of error with respect to the outcome of the algorithm, i. e. , a quantitative model to measure the sampling error, and to estimate the error efficiently. In this paper, based on the systematic analysis, the authors point out the deficiency of the current results in this field and give a novel, flexible quantitative model to measure the sampling error, and propose a high efficient computational method, interval estimation algorithm of cardinal error, for estimating sampling error based on the real observation and the theoretical analysis. Both of theoretical analysis and realistic experiments show the error between sample set and original dataset can be obtained effectively and accurately by the model, the representative capability of sample set to original dataset also can be estimated efficiently and exactly by the interval estimation algorithm of cardinal error. What's more, the interval estimation algorithm of cardinal error can be conveniently nested into sampling algorithms to speed up them and is useful for distributed, parallel association rule mining algorithms.
Keyword关联规则 频繁项集 取样误差 主误差 PAC学习
Indexed ByCSCD
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Document Type期刊论文
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GB/T 7714
贾彩燕,陆汝钤. 关联规则挖掘的取样误差量化模型和快速估计算法[J]. 计算机学报,2006,29.0(004):625-634.
APA 贾彩燕,&陆汝钤.(2006).关联规则挖掘的取样误差量化模型和快速估计算法.计算机学报,29.0(004),625-634.
MLA 贾彩燕,et al."关联规则挖掘的取样误差量化模型和快速估计算法".计算机学报 29.0.004(2006):625-634.
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