分类数据的英文

• categorical data
• cla ified data
• classified data
• cluster data
• grouped data

• "分类"英文翻译    classify
• "数据"英文翻译    data; record; information
• "分类数据方差" 英文翻译 :    variance of classified data
• "分类数据分析" 英文翻译 :    categorical data analysis
• "分类数据文件" 英文翻译 :    sorting data file
• "非数值数据 分类数据 分类资料" 英文翻译 :    categoricaldata
• "分类数" 英文翻译 :    number of categories
• "常规收集的病人分类数据" 英文翻译 :    routinely collected patient classification data
• "分类数据的检索设施" 英文翻译 :    retrieval facility
• "分组数据；分组资料；分类数据；分类资料" 英文翻译 :    grouped data; groupeddata
• "分类数组" 英文翻译 :    sorted array
• "不分组数据不分组资料不分类数据不分类资料" 英文翻译 :    ungrouped data; ungroupeddata
• "编类数据集" 英文翻译 :    catalogued data set
• "同类数据" 英文翻译 :    homogeneous data
• "未归类数据" 英文翻译 :    ungrouped data
• "异类数据" 英文翻译 :    heterogeneous data
• "类数" 英文翻译 :    class number
• "类数据库大同小异" 英文翻译 :    sql
• "语音类数字数据" 英文翻译 :    ddov digital data over voice
• "整数类数据类型" 英文翻译 :    integral type
• "航机与道面承压分类数系" 英文翻译 :    aircraft-pavement classification number system (acn-pcn)
• "数据分类" 英文翻译 :    classification of data; data category; data classification; data qualification; data sorting; digital sorting; numerical classification
• "数据分类机" 英文翻译 :    data cla ifier; data classifier
• "数据块分类" 英文翻译 :    block sort
• "被选数据分类" 英文翻译 :    sorting of selected data

• Rule - based outlier mining approach in categorical data
  基于规则的分类数据离群挖掘方法研究
• Breakdowns of statistics on employment
  就业分类数据
• Analysis of categorical data and its application in clinical trial
  分类数据的重复测量及其在临床试验中的应用
• Brokers usually extend the value proposition of a registry by offering intelligent search capability and business classification or taxonomy data
  中介者经常通过提供智能搜索能力和商业分级或分类数据来扩展注册处的价值取向。
• Facing the massive volume and high dimensional data how to build effective and scalable algorithm for data mining is one of research directions of data mining
  面对大规模的、高维的数据，如何建立有效的，可扩展的分类数据挖掘算法是数据挖掘领域的研究热点。
• Svm maps input vectors nonlinearly into a high dimensional feature space and constructs the optimum separating hyperplane in the spade to realize modulation recognition
  支撑矢量机把各个识别特征映射到一个高维空间，并在高维空间中构造最优识别超平面分类数据，实现通信信号的调制识别。
• This paper first illustrated some typical algorithms for large dataset , then gave off a processing diagram in common use second , for the dataset with large quantity and many attributes , we renovated the calculation method of the attribute ' s statistic information , giving off a ameliorated algorithm this thesis consists of five sections chapter one depicts the background knowledge and illustrates the position of data mining among many concepts also here is the data mining ' s category chapter two describes the thought of classification data mining technique , puts forward the construction and pruning algorithms of decision tree classifier chapter three discusses the problems of adapting data mining technique with large scale dataset , and demonstrates some feasible process stepso also here we touches upon the combination r - dbms data warehouse chapter four is the design of the program and some result chapter five gives the annotation the conclusion , and the arrangement of future research
  本论文的组织结构为：第一章为引言，作背景知识介绍，摘要阐述了数据挖掘在企业知识管理、泱策支持中的定位，以及数据挖掘的结构、分类；第二章讲述了分类数据挖掘的思路，重点讲解了泱策树分类器的构建、修剪，第三章针对大规模数据对数据挖掘技术的影响做了讲解，提出了可采取的相应的处理手段，以及与关系数据库、数据仓库结合的问题；第四章给出了论文程序的框架、流程设计，以及几个关键问题的设计；第五章对提出的设计进行简要的评述，做论文总结，并对进一步的研究进行了规划。
• 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。
• As a currency learning techniques introduced recent years , svms which handle small sample size problem have the features of good generalization ability , solid theoretical background , high accuracy , and getting global optimization . ho vever , it is a classifier for two - class originally and not suite for the multi - class problems and dealing with large data sets . on the other hand , , rst has the features of handling and reducing large data sets while has lower classification accuracy than svms . in this paper , the data are classified in advance with the rst , and two methods of combination of the data to classify two - class problems are proposed
  但它是二值分类器，不适用于多值分类场合及处理海量数据。粗集理论则具有处理和约简大数据量的优势，但分类精度不如svm方法。本文利用粗集理论对数据进行预分类，在此基础上提出两种二值分类数据组合方法，然后，再利用svm两两分类。

• 分类数据的日语：ぶんるいデータ