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By studying the algorithms of data-mining technology and taking full consideration of the fuzzy feature in the process of data-mining, it makes the classic mining technology fuzzy in order to show the inexactness and the ambiguity of things in the real world.

本文通过对数据挖掘技术方法的研究,并充分考虑挖掘过程中模糊性,对经典的挖掘技术加以模糊处理,体现了现实世界中事物特性的不确定性和模糊性。

For subjective interestingness measure, templates are used to differentiate the rules interested to the users or not.

引 言目前,数据挖掘技术受到科技界的广泛关注,关联规则挖掘是数据挖掘的一个重要内容。

Discovering association rules is one of more important tasks in data mining.One of the important problems is the evaluation of interestingness for the discovered rules.

关联规则挖掘是数据挖掘中重要的研究课题,而其中一个重要问题是对挖掘出的规则的感兴趣程度的评估。

The contributions are listed as follows:(1) a single-pass closed frequent itemsets mining process based on reverse lexicographic order FP-tree is introduced for mining classification association rules, which uses a kind of mixed item-ordering searching policy to satisfy the single-pass requirement of data streams and uses the bitmap technology to improve the efficiency;(2) the concept of self-support for filtering rules is proposed to improve the precision.

主要贡献在于:1提出了一种基于逆文法顺序FP-Tree的频繁闭项集单遍挖掘过程,用于挖掘类关联规则,该过程采用了一种混合项顺序搜索策略以满足数据流挖掘的单遍性需求,并采用位图技术提高效率:2提出了"自支持度"概念,用于筛选规则以提高算法分类精度。

The carriage positioning was finished, the coordinate transformation matrix of the bucket relative to the ground and the digging depth were obtained and it is easy to make the three-dimensional trace control of the hydraulic excavator bucket easy.

然后,推导该定位车体位姿下铲斗坐标系相对于大地的坐标变换矩阵,导出挖掘深度定位公式,实现了挖掘挖掘轨迹的三维空间定位,为实现挖掘机的三维空间轨迹精确控制与挖掘深度控制打下了基础。

Preserved for more than 5,700 years, the structure was found in an ancient peat bog next to the Belmarsh prison in Plumstead, a suburb of East London near the banks of the River Thames .

封存了5700年仍然如此完整,而在挖掘现场找到其中的原因了:因为挖掘现场是在普拉姆斯特德的贝尔马什监狱一个沼泽地里,这个是位于东伦敦泰晤士河旁边的一个郊区。挖掘现场是在位于东伦敦泰晤士河沿岸郊区Plumstead的Belmarsh监狱附近的古代泥炭沼泽地里。

Multi-dimension data visualization technology has been widely used in the world, and many common multi-dimension data visualization methods have been proposed, such as technology based on geometry, technology oriented ironic, technology based on layer, dense pels technology, and so on.

在本文中,我们将首先对数据挖掘技术、多维数据可视化技术、可视化数据挖掘技术进行介绍,然后我们会通过实例来介绍多维数据可视化技术在数据挖掘中的应用。

This paper combines Spearman Order Relational Coefficient and Rough Set with Data Mining, and provide a combined model of improving the efficiency of the Data Mining.

本文将Spearman 秩相关检验法、粗糙集与数据挖掘算法结合起来,提出了一种提高数据挖掘效率的组合数据挖掘模型。

At last, the paper discusses the function of user interface and gives the method of selecting constraints according to information plus, the operating flow of association rule mining system based on meta-knowledge, and the idea of inducting meta-knowledge in theevaluating phase in order to unit evaluating rule to whole procession, thus can do better in enhancing mining efficiency and veracity.

最后,提出了利用信息增益选择约束的方法,给出了基于元知识的关联规则挖掘系统的操作流程,探讨了可视化的功能实现问题,提出了在结果评价阶段将评价规则作为元知识统一到挖掘过程中的思想,以利于提高全系统的挖掘效率和准确性。

This paper is based on data mining technology,use weka as a library data mining software tool,use weka's j48 tree algorithm and data association analysis library data.mining useful data which user needs from the mass library data and get reasonable results.ultimately to improve work efficiency and scientific management.

该文以数据挖掘技术为基础,利用weka软件作为图书馆数据挖掘工具,通过weka里的j48树算法和数据关联等算法,对图书馆的馆藏数据进行相应的分析,从海量数据中挖掘出用户需要的有用数据,并得到合理的统计结果。最终达到提高工作效率,能够科学管理的目的。

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