QUERY OPTIMIZATION TECHNIQUE FOR DATA MINING

数据挖掘的查询优化技术

• 批准号: 10480066
• 负责人: MORISHITA Shinichi
• 金额: $ 6.66万
• 依托单位: UNIVERSITY OF TOKYO
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (B).
• 财政年份: 1998
• 资助国家: 日本
• 起止时间: 1998 至 2000
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-10480066/
• 关键词: DATA MINING; DATABASE QUERY OPTIMIZATION; ALGORITHM; COMPUTATIONAL COMPLEXITY; 知識発見

We have studied how to efficiently compute significant association rules according to common statistical measures such as a chi-squared value or correlation coefficient. For this purpose, one might consider to use of the Apriori algorithm, but the algorithm needs major conversion, because none of these statistical metrics are anti-monotone, and the use of higher support for reducing the search space cannot guarantee solutions in its the search space. We have developed a method of estimating a tight upper bound on the statistical metric associated with any superset of an itemset, as well as the novel use of the resulting information of upper bounds to prune unproductive supersets while traversing itemset lattices. Experimental tests demonstrated the efficiency of this method.

我们研究了如何根据常见的统计措施（例如卡方值或相关系数）有效计算重要的关联规则。为此，人们可能会考虑使用APRIORI算法，但是该算法需要进行重大转换，因为这些统计指标都不是抗单酮，并且使用更高的支持来减少搜索空间无法保证搜索中的解决方案。空间。我们已经开发了一种方法，可以估算与项目集的任何超集相关的统计指标上的紧密上限，以及在遍历项目集晶格的同时，新的上限信息的新颖使用中的上限信息来修剪无生产力的超级集。实验测试证明了该方法的效率。

项目成果

Akihiro Nakaya, Hideharu Furukawa and Shinichi Morishita: "Weighted Majority Decision among Several Region Rules for Scientific Discovery."In Proc.of Second Int'l Conf.on Discovery Science, Lecture Notes in Artificial Intelligence, Springer. 15-29 (1999)

Akihiro Nakaya、Hideharu Furukawa 和 Shinichi Morishita：“科学发现的几个区域规则之间的加权多数决定”。载于第二届国际发现科学大会论文集，人工智能讲义，施普林格。

Shinichi Morishita, and Akihiro Nakaya: "Parallel branch-and-bound graph search for correlated association rules."Proceedings of ACM SIGKDD Workshop on Large-Scale Parallel KDD Systems, San Diego, August 1999. (Revised version : Lecture Notes in Artificia

Shinichi Morishita 和 Akihiro Nakaya：“相关关联规则的并行分支定界图搜索。”ACM SIGKDD 大规模并行 KDD 系统研讨会论文集，圣地亚哥，1999 年 8 月。（修订版本：Artificia 中的讲义）

Takeshi Fukuda,Yasuhiko Morimoto,Shinichi Morishita,and Takeshi Tokuyama: "Mining Optimized Association Rules for Numeric Attributes"Journal of Computer and System Sciences-Special issue on the 15th ACM SIGACT-SIGMoD-SIGART Symposium on Principles of Data

Takeshi Fukuda、Yasuhiko Morimoto、Shinichi Morishita、Takeshi Tokuyama：“数值属性的挖掘优化关联规则”计算机与系统科学杂志-第15届ACM SIGACT-SIGMoD-SIGART数据原理研讨会特刊

Shinichi Morishita and Jun Sese: "Traversing Itemset Lattices with Statistical Metric Pruning"Proc.of ACM SIGACT-SIGMOD-SIGART Symp.on Database Systems (PODS). 226-236 (2000)

Shinichi Morishita 和 Jun Sese：“使用统计度量剪枝遍历项集格子”Proc.of ACM SIGACT-SIGMOD-SIGART Symp.on 数据库系统 (PODS)。

中谷明弘・森下真一: "ナノレベルのゲノム実験室" 情報処理. (採択済 発表予定).

Akihiro Nakatani 和 Shinichi Morishita：“纳米级基因组实验室”信息处理（已接受，计划出版）。