数据挖掘

词组短语

data mining

与数据挖掘相关的网络例句 [注：此内容来源于网络，仅供参考]

Using ontology and context knowledge in data mining is one of the effective waies to improve data mining accurateness,which can add general knowledge and certain knowledge in decision factors. How to apply ontology and context knowledge in data mining is discussed in this paper.

在数据挖掘中使用本体和上下文知识能够将普遍的知识和特定的知识引入数据挖掘的决策因素中，是增进数据挖掘准确性的有效手段，同时也是数据挖掘领域研究的热点和难点之一。

Using ontology and context knowledge in data mining is one of the effective waies to improve data mining accurateness,which can add general knowledge and certain knowledge in decision factors.

在数据挖掘中使用本体和上下文知识能够将普遍的知识和特定的知识引入数据挖掘的决策因素中，是增进数据挖掘准确性的有效手段，同时也是数据挖掘领域研究的热点和难点之一。

This paper summarizes the background and advantage of data mining, as well as the significance of data mining in the scientific research management of colleges and universities firstly, and then discusses the theory of data mining, association rules and the ideas of main algorithm, analyzes the classic Apriori algorithm and its existing problems as well as the basic solutions. After that, this paper proposes Multi-Dimensional Apriori algorithm which is designed specially for the mining of this paper; Then describes the structure of scientific research data mining system, defines subject-oriented mining tasks, including: mining the data about research projects, mining the data about papers, mining the data about academic writings. The association mining process is implemented by programing, a number of stimulating association rules are found, interpreted and analyzed.

本文首先综述了数据挖掘的研究背景、意义以及数据挖掘技术在高校科研管理中的应用现状和意义，然后在对数据挖掘相关理论、关联规则思想及主要算法进行讨论，分析经典Apriori算法及其存在的问题、基本解决方案后，提出了适合本文挖掘的多维Apriori算法的设计方案，并应用于本文挖掘中；接着论文介绍了科研数据的关联挖掘系统的结构，确定了面向主题的挖掘任务，包括：科研项目信息的挖掘、论文信息的挖掘、学术专著信息的挖掘等；设计了关联规则的实施过程，并通过程序编码得以实现，获得了多条有启发性的关联规则，并对其进行了解释与分析。

During the study of the subject, I have an all-around understanding about the theory and application of Data Mining through reading bookmaking and papers in this field.Based on the research on algorithms, we realized one example following the workflow of Data Mining, which helps us understand the application of the technology.KEYWORDS: data mining , association rule , apriori algorithm

在课题的研究过程中，我通过阅读大量国内外著作、论文，对数据挖掘技术的理论和应用有了一个较为全面的了解，在理论上对关联规则的挖掘算法进行了深入的研究，对于数据挖掘的应用通过实现具体的实例进行研究，并且对挖掘结果进行评价，从而对数据挖掘的应用步骤有了更加深刻的理解。

Object of data mining is service concepts of service bag, carrier of data mining is history data, and spirit of data mining is mining classification.

在对服务概念开发进行数据挖掘时，服务包中的服务概念是数据挖掘的对象，历史数据是数据挖掘的载体，分类挖掘是数据挖掘的指导思想。

The main contents are listed as follows: Analyse and research of the data mining technique. the dissertation summarizes,analyses and studies the current status of the data mining technique in our native country and overseas widely and roundly research . The definiens and orientation of the data mining is reviewed in brief first.

对数据挖掘技术的国内外研究现状进行了广泛而全面地归纳、分析和研究，对数据挖掘的定义及定位进行了简要的回顾，在数据挖掘基本概念的基础上，对数据挖掘常使用的技术和研究的对象进行了详细地分类、归纳和总结。

The dissertationsummarizes,analyses and studies the current status of the data mining technique inour native country and overseas widely and roundly and then summarizes anddiscusses its developmental trends and hot research fields. The definiens andorientation of the data mining is reviewed in brief first. Based on the basic conceptsof data mining,this dissertation classifies and summarizes the objects of data miningand the common techniques in detail.

对数据挖掘技术的国内外研究现状进行了广泛而全面地归纳、分析和研究，对数据挖掘技术的未来发展趋势和热点研究领域进行了总结和探讨，对数据挖掘的定义及定位进行了简要的回顾，在数据挖掘基本概念的基础上，对数据挖掘常使用的技术和研究的对象进行了详细地分类、归纳和总结。

Visualization technology can realize interactive analysis of direct data input, results output and mining, and provide ways of data mining with the participation of people's perception, insight and perspicacity, as well as ways of visualized mining and the visualization of mining results.

可视化技术能为数据挖掘提供直观的数据输入、结果输出和挖掘过程的交互探索分析手段，可以在人的感知力、洞察力、判断力参与下提供数据挖掘手段，实现可视化辅助挖掘手段以及挖掘结果的可视化。

Sixthly, it also introduces knowledge management technology to resolve the analysis of custom checking center operating flow, innovation research of Customs Business and consumer relationship management. It is a long-term worthy project to continue this study on the establishment of custom risk management by data mining. There are still many further works to do, such as more effective optimizing arithmetic, text data mining, super-huge data warehouse based on UNIX and Oracle, data mining application on web, building and application of cosmical database, custom knowledge management system, and so on.

基于数据挖掘的海关风险管理体系建设，是一个长期的、内容广泛的、需要进一步深入研究的庞大课题，本人希望在很多方面继续进行研究，例如：在海关引入效率更高的数据挖掘优化算法，探讨基于海关文档资料的文本数据挖掘，探讨基于Web的数据挖掘技术在海关的应用，探讨基于UNIX和Oracle基础之上的超大规模数据仓库建设和应用，探索建立基于数据挖掘的海关知识管理体系等。

The data model is the data source for the integrated mining which is built by using Snow Flake Schema. The integrated mining model consists of local mining and global mining. That is to say, firstly, monitoring cell mining, point mining and monitoring data mining are fulfilled respectively, then global mining model is run to opimize the monitoring process. The paper brings forward how to build up these models and the framework, and what variables should be delivered.

首先，采用雪花结构建立了数据模型构架，为流程挖掘提供数据底层；然后建立了监测流程综合挖掘模型构架，给出了监测单元挖掘、监测点位挖掘和监测数据挖掘，以及综合挖掘的模型函数和参数，以局部优化和全局优化的思路实现了土壤环境监测全流程优化。

与数据挖掘相关的网络解释 [注：此内容来源于网络，仅供参考]

Information Active Service：数据挖掘

信息服务:Information Service | 数据挖掘:Information Active Service | 信息咨询:Information consulting service

data information system：数据信息系统

数据挖掘系统:Data Mining System | 数据信息系统:data information system | 流式数据库系统:data steam system

Knowledge Discovery：数据挖掘

知识提取:Knowledge Discovery | 数据挖掘:Knowledge Discovery | 知识发现:Knowledge Discovery

date envelopment analysis：数据包络分析法

数据源划分:date resourse division | 数据包络分析法:date envelopment analysis | 数据挖掘技术:Date Mining Technology

franchise tax：特许税

40万个纳税人每年得特许税(Franchise tax)和营业税征收额都在1亿9千万美圆以上. 采用数据挖掘方法,每年Texas州利用数据挖掘技术从未申报的税收中发现百万计的偷逃税款.

multidimensional：多维

c) 多维(Multidimensional)包:包括表示多维数据资源的元数据的类与关联. (4) 分析(Analysis)包:它由以下五个子包组成:a) 转换(Transformation)包:包括表示数据抽取和转换工具的元数据的类与关联. c) 数据挖掘(Data Mining)包:包括表示数据挖掘工具的元数据的类与关联.

simplicity：简单性

兴趣度测量原语包括:简单性(simplicity);确定性(certainty,比如:可信度);效用(utility,比如:支持度);新颖性(novelty). DMQL正是基于这些原语设计的数据挖掘查询语言. 它允许从关系数据库和数据仓库中多个抽象层次上特殊(ad-hoc)和交互地挖掘多种种类的知识.

Data Warehousing：数据仓库

11.9.8 数据仓库(Data Warehousing)和数据挖掘(Data Mining)数据挖掘(Data Mining)是对数据仓库中的数据进行进一步处理以得到更有用信息的过程. 数据挖掘工具被用来发现数据的联合和相关性来生成元数据(Metadata). 元数据可以单个信息子集中隐含的相关性.

data snooping：数据侦察

data mining 数据挖掘 | data snooping 数据侦察 | consumption beta 消费贝塔

KED7 Data Mining: Display Form：数据挖掘: 显示表格

KED6 Data Mining: Change Form 数据挖掘: 更改格式 | KED7 Data Mining: Display Form 数据挖掘: 显示表格 | KEDA Export Summarization Level 输出汇总层次