data mining tools中文是什么意思

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中文翻译
造句

数据挖掘工具

data mining 【计算机】数据挖掘。
tool n. 1.工具，用具，器具；【机械工程】刀具；工具母机 ...
reed mining tools inc 里德采矿工具有限公司
cemented carbide for coal-mining tools 煤炭采掘工具用硬质合金制品
data processing tools 数据处理工具
data replication tools 数据复制工具
data fusion and mining methods 数据融合与数据挖掘
data mining and knowledge discovery 数据发掘与知识发现; 数据开发与认知; 数据挖掘和知识发现
data mining and visualisation 数据挖掘和可视化
data mining specialist 挖掘师
dm data mining 数据提取; 数据挖掘
intelligent data mining 智能数据挖掘
introduction to data mining 挖掘导论课堂笔记
knowledge discovery and data mining 知识发现和数据挖掘
msc data mining and knowledge management 数据挖掘和知识管理
tools 装配工具
mining n. 1.采矿，采矿业，矿业。 2.敷设地雷[水雷]。 adj. 开矿的，采矿的。 a mining engineer 采矿工程师。 mining engineering 采矿工程(学)。 the mining industry 采矿工业。
abrasive tools 研磨工具
advanced tools 高级工具（什么工具？我没觉出来）
air tools 风动工具; 气动工具
aligning tools 校准工具
alignment tools 校直工具
analysis tools 刀轨分析工具; 分析工具
analytic tools 分析工具
animation tools 动画工具

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例句与用法

We put forward a product conceptual design model based on data mining tool , and explore the product function design and mapping of function and structure based on data mining technology
摘要提出了基于数据挖掘工具的产品概念设计模型，探讨了产品功能设计和基于数据挖掘技术的功能结构映射。
In general , this thesis is based on the basic theory and technology of cluster analysis in data mining tool , puts forward some new viewpoint and algorithm designing notation and attempts to discuss in theory and practice , combining the present practical application
概括而言，本文以数据挖掘工具中聚类分析的一般理论和技术为基石，结合目前实际应用，提出了一些新的观点及算法设计思路，并试图在理论和实践两方面作出论述。
Relative to separate data mining tool , data mining integration system is economical and believable . now , the problem is how to integrate the application for data mining , while different companies " information systems have different interface , framework and software edition
对于拥有分布式数据库的大型企业而言， 2中所提到的问题都可以解决，因为企业所使用的it系统可以事先制定统一的接口、架构、版本，一切问题似乎都解决了。
First , the current situation of the international and domestic customer segmentation was elaborated , and the features and shortcomings of the present mainstream data mining tools were described . according to the telecom industry ’ s huge amount data and high multi - dimension , the principle of k - means was elaborated , the optimization of the initial value and the improvement of running efficiency were considered as the key points of the algorithm innovation
结合电信领域的海量数据及数据高维度特点,重点阐述了直接k - means算法的设计思想,确定了以优化初值和提高执行效率作为算法改进的关键点,提出了适合于通信行业客户细分的k - m - d算法思想,并完成该算法的详细设计。
With the explosive growth of information sources on the world wide web , it has become increasingly necessary for e - commerce companies , who have transfer the web into the primary tool for its development , to utilize data mining tools to find , extract and analyze user access patterns in their e - commerce websites
由于www资源的爆炸性增长，对于已经把web转化为关键发展工具的电子商务来说，运用数据挖掘技术获取用户的访问模式对于电子商务网站的生存发展是十分有益的。
In chapter 4 , we give a new approach for inference risk evaluation , which uses the rough set theory in data mining technique to catch the possible inference channel in mls dbms . compared with other methods , the main advantages lie in that it does n ' t need the knowledge of sso ( system security officer ) and can use the current available data mining tools to monitor the database in time . 4
在总结已有的研究成果的基础上，利用数据挖掘技术中粗糙集（ roughset ）理论提出了一种对推理风险进行分析的量化方法，优点是不依赖于系统安全员的知识、能够处理确定性的和非确定性的推理通道的，利用相应工具，能够在数据库运行期间对推理风险进行监督。
A extenic spatial data mining tool , is also developed , using the arcview , a gis software which is supported by the esri , for the purpose of the real - time debris flow danger degree zoning of the debris flow danger degree . user can interact with the system through this tool , and the result can also be visualized
作者首次将可拓工程方法与空间数据挖掘技术相结合，是对空间数据挖掘方法的丰富，所构建的可拓空间分类器，初步形成了对泥石流信息系统空间数据库进行深度挖掘和知识再发现、再表达能力。
With the rapid increasement of data , the enterprises want to mine the knowledge behind the large amounts of data in order to support decision . some existing data mining tools such as intelligence miner , enterprise miner supply rich data mining functions , but these tools can ’ t mine distributed and heterogeneous data on internet / extranet , and they can ’ t integrate with operating system effectively and have no pertinences
现有的数据挖掘工具如ibm的intelligenceminer , sas的enterpriseminer虽然提供了较丰富的挖掘功能,但是这些工具不能够挖掘internet / extranet上的分布式和高度异质的数据,不能有效地与操作型系统集成,而且针对性不强。
The current data mining research has transported its emphasis from the discovery method to system application gradually . it emphasis on discovering strategies based on multi - methods and the technology integration , as well as mutual seepage between different disciplines , the general data mining tool has no longer adapted the developing data mining application demand
当前数据挖掘研究重点逐渐从发现方法转向系统应用,注重多种发现策略和技术的集成,以及多种学科之间的相互渗透,一般的数据挖掘工具已经不再适应数据挖掘应用发展的需求。
With the explosive growth of information sources on the world wide web ， it has become increasingly necessary for e - commerce companies ， who have transfer the web into the primary tool for its development ， to utilize data mining tools to find , extract and analyze user access patterns in their e - commerce websites , in this paper , we use the data mining technology on the web site to find user access patterns , and use user access patterns to improve web site ' s structure and content timely and satisfiy the users " need of personalization
由于web资源的爆炸性增长，对于已经把web转化为关键发展工具的信息网站（特别是电子商务网站）来说，运用数据挖掘技术获取用户的访问模式对于网站的生存是十分有利的。本文把数据挖掘技术应用于信息化网站，得到web用户访问模式，并利用用户访问模式及时地对网站的内容和结构进行完善和改进，以满足用户的个性化需求。

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Last modified time:Sun, 17 Aug 2025 00:29:56 GMT