多值属性的英文

• multiple-valued attribute

• "多值"英文翻译    multiple-valued
• "属性"英文翻译    attribute; nature; property; ...
• "值属性" 英文翻译 :    value attribute
• "单值属性" 英文翻译 :    single-valued attribute
• "个体值属性" 英文翻译 :    individual-valued property
• "价值属性" 英文翻译 :    property of value
• "客体值属性" 英文翻译 :    object property
• "任务值属性" 英文翻译 :    task value attribute
• "数据值属性" 英文翻译 :    data-valued property
• "数值属性" 英文翻译 :    numerical attribute
• "值属性表" 英文翻译 :    attribute table vat; attribute table, vat
• "标准数值属性" 英文翻译 :    standard numerical attribute
• "系统数值属性" 英文翻译 :    system numerical attributes
• "置值属性的星号用法" 英文翻译 :    asterisk with set attribute
• "多值" 英文翻译 :    [数学] multiple-valued◇多值法 multivalue method; 多值函数 many-valued [multiple-valued; multivalued] function
• "裂值属" 英文翻译 :    schistogonimus
• "多值的" 英文翻译 :    many-valued; multi-value; multiple valued; multiplevalued; n valued; n-valued
• "多值法" 英文翻译 :    multivalue method
• "多值性" 英文翻译 :    ambiguity
• "多值锥" 英文翻译 :    cone; ambiguity
• "属性" 英文翻译 :    [计算机] attribute; nature; property; quality 列式属性 alignment attribute; 事件属性 event attribute; 综合属性 synthesized attribute
• "多值触发器" 英文翻译 :    multiple-valued flip-flop
• "多值代数" 英文翻译 :    mv-algebra
• "多值关系" 英文翻译 :    multivalued relation
• "多值函数" 英文翻译 :    many valued function; many-valued function; multiform function; multiple valued function; multiple-value function; multivalue function; multivalued function

• Multi - valued attributes i . e . those declared with the
  多值属性例如那些使用
• You can perform an action whenever an element is deleted from a multi - valued attribute by specifying a delete trigger , like this
  当一个成员从多值属性中被删除时，我们可以定义一个删除触发器：
• You can perform an action whenever the value of a single - valued attribute or an element of a multi - valued attribute is replaced , like this
  当一个单值的属性值或者多值属性的成员被替换时，我们可以定义一个替换触发器：
• If this flag is not present , all of the values , up to a server - specified limit , in a multi - valued attribute are returned when any value changes
  如果没有此标志，则当任何值发生更改时，将返回多值属性中的所有值（最多到服务器指定的限制） 。
• The aim which rough set theory study is a aim set that is described by a muti - value attribution . for every aim and its attribution , there has a value as its described charter aim , attribution and its described charter are the three basic factors to expression decision problems
  Rough集的研究对象是由一个多值属性(特征，症状，特性)集合描述的一个对象集合，对于每个对象及其属性都有一个值作为其描述符号，对象，属性和描述符是表达决策问题的3个基本要素。
• This part put forward the system conception of kdd and the apriori algorithm . then evolved the create - frequent - set algorithm which was fit for the freight agent management system . because of the shortage of efficiency , 1 improved the algorithm . because some of the items were not boolean variables , 1 need the quantitaitve attributes association rules discovering algorithm . in general , there had the levels among the items , so multilevel association rules existed . after perfecting the algorithmic need interpret and evaluate the knowledge . in the end , 1 discussed the privacy and security of kdd . the fifth part described the future problems and prospect
  第四章是论文的主体，着重介绍知识发现的全过程，按照semma方法论首先进行数据准备，然后进入数据挖掘阶段，提出知识发现的概念体系和公认的apriori算法，从该算法演变出适合于货代管理系统的生成频繁项目集的算法；因为在实际应用中存在效率上的不足，因此进一步地提出了改进方案；在事务处理中各个项目并不都是布尔型变量，因此需要特定的针对多值属性的关联规则发现算法；通常情况下，项目之间存在有层次关系，因此多层次关联规则的发现普遍存在；算法完善并运行后需要对发现的知识进行解释和评估；本章的最后讨论了知识发现的私有性和安全性问题；第五章讲述有待解决的问题和发展前景。
• The decision tree had a lot of algorithms , this paper focus on the optimization of fast classification in the face of n - value attribute of id3 algorithm which had parameters of user ' s interest . on the basis of avoiding the weak relevant attribute of n - value covered the worth strong relevant attribute , simplify complexity of the original algorithm and code cost through the mathematics tool , thus raise the speed of operation while using this algorithm , and lower costs in thrift as much as possible , to raise the efficiency
  决策树学习有很多算法,本文着重研究了对引入用户兴趣度参数的id3算法在面对多值属性时的快速分类的优化,在避免了多值弱相关属性覆盖少值强相关属性的基础上,通过数学工具简化原算法的复杂度和编码代价,从而提高使用该算法时的运算速度,尽量多的节约计算时间,从而达到降低成本的,提高效率的目的。
• The first chapter in this paper provides a survey of data mining technology , and explains basic concepts , function and the whole framework of data mining and difficulties in developing and some future directions in association rule generation ; the second chapter introduce the basic concepts , brings forward a classification of association rule ; the third chapter give a deep research on algorithms of every kind of association rule , include mining single - dimensional signal - level association rule and multidimensional multilevel association rule , it describes these algorithm , point out some method to optimize this algorithm and test its quality with experiments ; the fourth and fifth chapter introduce the designs about association rule mining system basing on relation database visual foxpro in detail : according to system frame of the association rule mining , actualize a new mining algorithms and analyses every function module of program , at last further analyses the left problems in designs
  本论文第一部分对数据挖掘技术进行了总体介绍，说明了基本概念、功能和系统总体框图以及发展中的难点和研究方面；第二章对关联规则基本概念的进行了介绍，提出了关联规则的分类方法；第三章探讨了挖掘各种关联规则的算法，从挖掘单维单层布尔关规则的经典的apriori开始，分析了挖掘单维、多层关联规则的算法，多维关联规则的算法到多维多值属性关联规则的算法。文中提出算法优化方法，并对其性能进行了实验测试；第四部分、第五部分详细介绍了基于关系型数据库的关联规则挖掘系统的设计构思，根据关联规则挖掘系统结构框架，实现了基于visualfoxpro的关联规则挖掘系统，其于采用了一个新型的基于关系数据库的关联规则挖掘算法，提高了挖掘效率，并详细分析了程序设计的各个功能模块，最后就设计中遗留的问题进行了进一步的分析。