数据挖掘工具的英文

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英文翻译
造句

data mining tools

数据 data; record; information
挖掘 excavate; unearth; dig; tap; ...
工具 tool; means; implement; appl ...
挖掘工具 digging tool; excavating tools
和数据挖掘 data mining
数据挖掘 dm data mining; kdd ontology
【计算机】数据挖掘。 data mining
数据挖掘技术 data mining
智能数据挖掘 intelligent data mining
数据挖掘和可视化 data mining and visualisation
数据挖掘扩展插件 dmx
挖掘工 digger; shoveler; shoveller; shovelman
数据融合与数据挖掘 data fusion and mining methods
数据挖掘和知识发现 data mining and knowledge discovery
数据挖掘和知识管理 msc data mining and knowledge management
知识发现和数据挖掘 knowledge discovery and data mining
挖掘；挖掘工程；开挖 excavation
挖掘工程 evacuation works; excavation works
挖掘工人 excavator
挖掘工作 spadework
数据挖掘及其在医学中的应用 data mining and its application in medical science
数据挖掘与知识发现的方法与实例 data mining and knowledge discovery methods with case examples
采掘工具 digging tool
大型挖掘工程 bulk excavation works
壕坑挖掘工程 trenching works

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例句与用法

Data miners will make great contributions to various fields like business , repository , science and medicine etc by analyzing data and discovering important patterns of data
数据挖掘工具可以进行数据分析，发现重要的模式，对商务决策，知识库，科学和医学研究做出重大贡献。
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。
The most important function of this system is to analysis the items in the transaction database , and to find association rules among the items on sale , then represents them in a simple and intuitionistic way to help decisions - making
数据挖掘工具中要实现的一个很重要的功能就是对交易数据库中的商品进行分析，发现顾客购买商品之间的关联性。
After comparing all kinds of tools for data mining provided by sas , we select methods adapt to the task , and all these algorithm is applied in the project of general custom for customs directly under general custom to execute the law and evaluate work . the result is satisfied
在对sas提供的各种数据挖掘工具和方法比较之后，选择了适合本课题需要的方法应用于海关总署直属海关执法评估系统的开发项目中，并取得了满意的结果。
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。
Considering a series of problems , the author brings forward an advanced data mining idea looking on enterprise application by in first analyzing the business problem of the medicine vocation , and needing technique to solve these question , next analyzing the system structure of data mining , data mining instrument abroad and domestic , enterprise practice application as well
针对这一系列问题，作者在首先分析了医药行业需要解决的商业问题，以及解决这些问题所使用的技术，和分析现有数据挖掘系统结构和国内外的数据挖掘工具以及国内企业的实际应用的基础上，提出了一种应用先进的数据挖掘思想面向企业应用主题的数据挖掘应用系统设计思想。
This article forcus on applying of data mining and establinshing csi weight model in crm system of retail business . estabilish crm system based upon data mining and design several data mining models . the concept of this report will bring new idear to business enterprices , help them to improve customer satisfaction and win competition
本文重点研究数据挖掘技术在零售业客户关系管理系统中的应用，对基于数据挖掘的零售业客户关系管理系统进行了设计，建立了零售业客户价值、客户满意度和客户细分的数据挖掘模型，并运用数据挖掘工具sasenterpriseminer对模型进行了验证和评价。
Data warehouse is a hot research area in 90s its main motif is to provide the decision - maker a powerful tool : gathering the data in pure consistent , relevant pattern , and making use of the data in managing analyzing , data - mining purposec that means that the decision - maker can use the tool to understand , grasp the situation of the business from different directions and forecast the future of it when using data warehouse , the processing speed determines data warehouse ' s practicability and processing ability the hoc ( highway decision center ) system realized before solves some key problems about intermediate scale data , mainly concentrating data warehouse performance coefficient when using hdc in large scale data , it encountered processing speed problem then the settlement of this problem becomes a major research point so , based on the former research achievements , the present task is to construct the renowned data warehouse architecture and its relevant algorithms , then adapts the system to the large scale dataset with data mining functions c this paper is a part of the research in order to construct the powerful system , a key problem is to cope with the processing - speed problem and the data space problem , etc , - caused by the large scale dataset and magnificent dataset this is also the core in the present data mining research this paper ' s motive is to design and realize a decision - tree classifier in the data warehouse system for large - scale dataset
大型数据仓库的处理速度问题目前是制约其推广应用的关键所在，也是这一领域的一个重要研究课题，也正是我们当前工作的重点：在前期研究工作的基础上围绕提高大型数据仓库处理速度问题，建立改进的数据仓库系统模型和相关算法，开发出面向中级以上企事业单位的、具有数据挖掘和分析能力的大型数据仓库系统。建立大型数据仓库所面临的关键问题，是如何妥善解决实际业务数据的大规模、海量特征所带来的处理速度和空间等问题，这也是当前挖掘技术研究必然面对的核心问题。本研究的目的是设计并实现大型数据仓库系统中的分类数据挖掘工具? ?决策树分类器，主要工作是在综合了解现有决策树分类算法的研究情况的前提下，对决策树算法适应大规模数据集的问题进行探讨，力求设计出能较好地适应大规模数据的分类器算法。
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。