多值屬性的英文

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英文翻譯
造句

multiple-valued attribute

多值 multiple-valued
屬性 attribute; nature; property; ...
值屬性 value attribute
單值屬性 single-valued attribute
個體值屬性 individual-valued property
價值屬性 property of value
客體值屬性 object property
任務值屬性 task value attribute
數據值屬性 data-valued property
數值屬性 numerical attribute
值屬性表 attribute table vat; attribute table, vat
標準數值屬性 standard numerical attribute
系統數值屬性 system numerical attributes
置值屬性的星號用法 asterisk with set attribute
多值 [數學] multiple-valued◇多值法 multivalue method; 多值函數 many-valued [multiple-valued; multivalued] function
裂值屬 schistogonimus
多值的 many-valued; multi-value; multiple valued; multiplevalued; n valued; n-valued
多值法 multivalue method
多值性 ambiguity
多值錐 cone; ambiguity
屬性 [計算機] attribute; nature; property; quality 列式屬性 alignment attribute; 事件屬性 event attribute; 綜合屬性 synthesized attribute
多值觸發器 multiple-valued flip-flop
多值代數 mv-algebra
多值關系 multivalued relation
多值函數 many valued function; many-valued function; multiform function; multiple valued function; multiple-value function; multivalue function; multivalued function

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例句與用法

Multi - valued attributes i . e . those declared with the
多值屬性例如那些使用
You can perform an action whenever an element is deleted from a multi - valued attribute by specifying a delete trigger , like this
當一個成員從多值屬性中被刪除時，我們可以定義一個刪除觸發器：
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
當一個單值的屬性值或者多值屬性的成員被替換時，我們可以定義一個替換觸發器：
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
如果沒有此標志，則當任何值發生更改時，將返回多值屬性中的所有值（最多到服務器指定的限制）。
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集的研究對象是由一個多值屬性(特征，癥狀，特性)集合描述的一個對象集合，對于每個對象及其屬性都有一個值作為其描述符號，對象，屬性和描述符是表達決策問題的3個基本要素。
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
第四章是論文的主體，著重介紹知識發現的全過程，按照semma方法論首先進行數據準備，然后進入數據挖掘階段，提出知識發現的概念體系和公認的apriori算法，從該算法演變出適合于貨代管理系統的生成頻繁項目集的算法；因為在實際應用中存在效率上的不足，因此進一步地提出了改進方案；在事務處理中各個項目并不都是布爾型變量，因此需要特定的針對多值屬性的關聯規則發現算法；通常情況下，項目之間存在有層次關系，因此多層次關聯規則的發現普遍存在；算法完善并運行后需要對發現的知識進行解釋和評估；本章的最后討論了知識發現的私有性和安全性問題；第五章講述有待解決的問題和發展前景。
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
決策樹學習有很多算法,本文著重研究了對引入用戶興趣度參數的id3算法在面對多值屬性時的快速分類的優化,在避免了多值弱相關屬性覆蓋少值強相關屬性的基礎上,通過數學工具簡化原算法的復雜度和編碼代價,從而提高使用該算法時的運算速度,盡量多的節約計算時間,從而達到降低成本的,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
本論文第一部分對數據挖掘技術進行了總體介紹，說明了基本概念、功能和系統總體框圖以及發展中的難點和研究方面；第二章對關聯規則基本概念的進行了介紹，提出了關聯規則的分類方法；第三章探討了挖掘各種關聯規則的算法，從挖掘單維單層布爾關規則的經典的apriori開始，分析了挖掘單維、多層關聯規則的算法，多維關聯規則的算法到多維多值屬性關聯規則的算法。文中提出算法優化方法，并對其性能進行了實驗測試；第四部分、第五部分詳細介紹了基于關系型數據庫的關聯規則挖掘系統的設計構思，根據關聯規則挖掘系統結構框架，實現了基于visualfoxpro的關聯規則挖掘系統，其于采用了一個新型的基于關系數據庫的關聯規則挖掘算法，提高了挖掘效率，并詳細分析了程序設計的各個功能模塊，最后就設計中遺留的問題進行了進一步的分析。