數值屬性 的英文怎麼說

中文拼音 [shǔzhízhǔxìng]
數值屬性 英文
numerical attribute
  • 數 : 數副詞（屢次） frequently; repeatedly
  • 屬 : 屬名詞1 （類別） category 2 [生物學] （生物分類系統上所用的等級之一） genus 3 （家屬; 親屬） fami...
  • 性 : Ⅰ名詞1 （性格） nature; character; disposition 2 （性能; 性質） property; quality 3 （性別） sex ...
  • 數值 : numerical value; numerial number; figure; magnitude; value數值表 numerical tabular; 數值天氣預報 ...
  1. Optimized association rules are permitted to contain uninstantiated attributes. the optimization procedure is to determine the instantiations such that some measures of the roles are maximized. this paper tries to maximize interest to find more interesting rules. on the other hand, the approach permits the optimized association rule to contain uninstantiated numeric attributes in both the antecedence and the consequence. a naive algorithm of finding such optimized rules can be got by a straightforward extension of the algorithm for only one numeric attribute. unfortunately, that results in a poor performance. a heuristic algorithm that finds the approximate optimal rules is proposed to improve the performance. the experiments with the synthetic data sets show the advantages of interest over confidence on finding interesting rules with two attributes. the experiments with real data set show the approximate linear scalability and good accuracy of the algorithm

    優化關聯規則允許在規則中包含未初始化的屬性.優化過程就是確定對這些屬性進行初始化，使得某些度量最大化.最大化興趣度因子用來發現更加有趣的規則;另一方面，允許優化規則在前提和結果中各包含一個未初始化的數值屬性.對那些處理一個數值屬性的演算法進行直接的擴展，可以得到一個發現這種優化規則的簡單演算法.然而這種方法的性能很差，因此，為了改善性能，提出一種啟發式方法，它發現的是近似最優的規則.在人造數據集上的實驗結果表明，當優化規則包含兩個數值屬性時，優化興趣度因子得到的規則比優化可信度得到的規則更有趣.在真實數據集上的實驗結果表明，該演算法具有近似線性的可擴展性和較好的精度
  2. The researchers then focused on the genetic profiles, or genotypes, of more than 1, 200 men and women whose systolic ( the higher number in a bp reading ) and diastolic ( lower number ) measurements fell at the extreme ? highest and lowest ? percentiles of distribution

    然後研究人員就開始研究收縮壓（血壓讀數中高的數值）和舒張壓（血壓讀數中低的數值）屬于非常高或非常低水平的1200位男性和女性病人的基因表型。
  3. This paper discusses the methods of similarity measurement of most clustering algorithms, and taking the type of attribute as a standard of choosing similarity, it expounds the methods used to measure numerical attribute, categorical attribute and mixed attribute

    討論了在大多數聚類演算法中的相似性測量方法，並以屬性的類型作為選擇相似性的標準，闡述了用於數值屬性，符號屬性及混合屬性相似性測量方法。
  4. In unmanaged metadata, the constant table is used to store constant values for fields, parameters, and properties

    在非託管元數據中，常數表用於存儲欄位參數和屬性property的常數值。
  5. C requires accessor methods for setting and retrieving property values that accept parameters

    C #需要訪問器方法來設置和檢索接受參數的屬性的值。
  6. A property or method call cannot include a reference to a private object, either as an argument or as a return value

    無論是作為參數還是作為返回值，屬性或方法調用都不能包括對私有對象的引用
  7. Tests whether all numeric properties of this

    的所有數值屬性是否都具有零值。
  8. Object to zero and its string properties to zero - length strings

    對象的數值屬性重置為零，並將其字元串屬性設置為零長度字元串。
  9. Discretization : for numeric attributes, sometimes it is not useful to display each distinct value of an attribute

    離散化：對于數值屬性，顯示屬性的每個非重復值通常毫無意義。
  10. In the process of data mining, there exists a sharp boundary problem if using intervals to deal with quantitative attributes, so we introduce fuzzy sets to solve this problem, and experiment results approve the feasibility of using fuzzy association rules and fuzzy frequency episodes to detect anomalies

    在數據挖掘過程中，由於利用間隔來處理數值屬性容易產生尖銳的邊界問題，我們引入模糊集的概念到數據挖掘演算法來解決這個問題，給出了具體的演算法，並通過實驗證實了利用模糊關聯規則和模糊頻繁序列檢測異常的可行性。
  11. 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演算法，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、多層關聯規則的演算法，多維關聯規則的演算法到多維多值屬性關聯規則的演算法。文中提出演算法優化方法，並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於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，實現了基於visualfoxpro的關聯規則挖掘系統，其于採用了一個新型的基於關系數據庫的關聯規則挖掘演算法，提高了挖掘效率，並詳細分析了程序設計的各個功能模塊，最後就設計中遺留的問題進行了進一步的分析。
  12. True and the integer - valued properties to their default values

    ，並將整數值屬性設置為它們的默認值。
  13. And sets the integer - valued properties to the default values shown in the following table

    並將整數值屬性設置為下表中顯示的默認值。
  14. 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

    決策樹學習有很多演算法，本文著重研究了對引入用戶興趣度參數的id3演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演算法的復雜度和編碼代價，從而提高使用該演算法時的運算速度，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。
  15. We found id3 algorithm is better than min - ambiguity in training accuracy, testing accuracy and size of tree by experimental and theoretical analysis. meanwhile, we propose a new heuristic

    通過實驗與理論分析，發現fuzzyid3演算法應用於符號值屬性類分明的數據庫時從訓練準確度、測試準確度和樹的規模等方面都要優于min - ambiguity演算法。
  16. 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，然後進入數據挖掘階段，提出知識發現的概念體系和公認的apriori演算法，從該演算法演變出適合於貨代管理系統的生成頻繁項目集的演算法；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並不都是布爾型變量，因此需要特定的針對多值屬性的關聯規則發現演算法；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；演算法完善並運行后需要對發現的知識進行解釋和評估；本章的最後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。
  17. Weighting for fuel economy and four emission performance determine an overall impact function, the user can adjust the target of the strategy by adjusting their weightings

    遺傳實時控制策略目標函數的屬性有燃油經濟性和四種排放物的排放量，通過對各個屬性權值的調整，實現對控制目標的調整。
  18. Gets or sets the metadata property value defined by the

    控制項定義的元數據屬性值。
  19. The beauty of the math is a kind of value property of the taste object of math satisfying the demand of the taste subject of math

    數學是數學審美客體滿足數學審美主體需要的一種價值屬性，其本質是數學的環境與憧憬數學的人們的意向的融合。
  20. Dialog box to bind single - value properties such as the

    使用「數據綁定」對話框可以將單值屬性（如
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