Nombre Claude Issa, Brou Konan Marcellin, Kimou Kouadio Prosper
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引用次数: 0
摘要
自参考算法APRIORI [AGR97]以来,已经开发了其他优化关联规则提取的算法。但没有一种方法通常比其他方法更好。本文讨论了高度相关数据环境下封闭项集的优化问题。本文中的工作回应了我们题为“优化频繁2项集提取的新方法”的文章中的一个观点。在上一篇文章中,我们在提取分散数据(弱相关数据)的上下文中从2项集获得了有趣的优化结果。本文允许我们在密集数据(强相关)上获得有趣的2项集结果。我们的方法受到{Pas00, CB02, BBR03, CF14]研究工作的启发。它通过在公式δ δ Ferm (S)-δ 0中引入由参数定义的误差范围,改进了简明数量的关联规则的提取,其中Ferm (S)是公式的闭包函数
Since the reference algorithm APRIORI [AGR97], other algorithms for optimizing the extraction of association rules have been developed. But no method is generally better than the others. This article deals with the optimization of closed itemsets in the context of highly correlated data. The work in this article responds to one of the perspectives of our article entitled "A new approach to optimizing the extraction of frequent 2-itemsets". In this previous article, we had obtained interesting optimization results from the 2-itemsets on a context of extraction of scattered data (weakly correlated data). The present article allowed us to obtain interesting results of the 2-itemsets on dense data (strongly correlated). Our approach was inspired by the research work of {Pas00, CB02, BBR03, CF14]. It has improved the extraction of a concise number of association rules by introducing a margin of error defined by the parameter in the formula δ δ Ferm (S)-δ <ε (δ an integer, δ>0, Ferm (S) is the -Closure of the
期刊介绍:
IJCSA is an international forum for scientists and engineers involved in computer science and its applications to publish high quality and refereed papers. Papers reporting original research and innovative applications from all parts of the world are welcome. Papers for publication in the IJCSA are selected through rigorous peer review to ensure originality, timeliness, relevance, and readability.