dimensional attribute 中文意思是什麼

dimensional attribute 解釋
維屬性
  • dimensional : adj. 1. 尺寸的。2. 空間的。3. 【數學】因次的；…次（元）的。
  • attribute : vt. 1. 把（某事）歸因於…。2. 認為…系某人所為。n. 1. 屬性，特質。2. （人物、官職等的）標志，表徵。3. 【語法】屬性形容詞。
  1. We can attribute this to the three dimensional nature of the problem.

    我們把此歸結為問題的三維本性。
  2. Based on the theory of cooperation between foundation and structure, paper uses ansys finite element software forming three - dimensional finite element model of. paper also anglicized element type choose, material attribute choose, loading determination, terminal condition determination, anyslys type determination and solution choose, etc. correcting buildings with the method of drawing out soil was analyzed through ninth aspects listed below

    本文借鑒結構設計中的基礎與上部結構共同作用理念，運用ansys有限元分析軟體，在探討了單元類別的選擇、材料屬性選擇、載荷、邊界條件和分析類別的確定、求解器、求解方法與收斂準則的選擇等與有限元建模相關內容的基礎上，建立了上部結構和土體共同作用的三維有限元模型。
  3. In this paper, we firstly present the whole framework of the system, including the introduction of the main functional module. next, in the part of data preprocessing, we design a method of collecting click - stream data in the application server layer and preprocessing them with real time ; in the part of data mining that is data analyzing, we research and implement an extended attribute - oriented induction algorithm which applies to data generalization analysis, and that, we also design and implement an hybrid - dimensional association rule mining algorithm for associative analysis. in the end, on the e - business web site system of jiangsu changjiang electronic group corp, we design and implement an intelligent dss ( idss ) with the help of the above algorithms

    論文首先給出了系統的整體框架體系結構設計，以及主要的功能模塊介紹；接著，在數據預處理部分，設計了在應用層收集點擊流數據並且對其進行實時預處理的方法；在數據挖掘即數據分析部分，研究與實現了用於數據概化分析的面向屬性規約的擴展演算法，以及設計並實現了用於關連分析的混合維關聯規則挖掘演算法；最後，在江蘇長江電氣集團的電子商務網站系統上，利用已分析的演算法設計並實現了一個智能決策支持系統。
  4. Firstly, this paper improves single dimensional association rule mining algorithm aprioritidlist based on deep research on association rule mining algorithms, and advances an efficient multidimensional association rule mining algorithm aprioritidlist + that is suitable for vulnerability database of rdbms. furthermore, the algorithm is applied on vulnerability database including data preparation, implement of the algorithm and analysis of experiment results, where data preparation is mainly to select some from numerous vulnerabilities and vulnerability attributes that are suitable for association rule mining to do experiments, meanwhile do the discrete process on quantified attribute values

    本文首先在深入研究關聯規則挖掘演算法的基礎上，對其中的單維關聯規則挖掘演算法aprioritidlist進行改進，提出了一種適合關系型弱點數據庫的高效的多維關聯規則挖掘演算法aprioritidlist + ;並且將該演算法應用到弱點數據庫中，包括數據準備、演算法實現和實驗結果的分析，其中數據準備主要是對數量龐大的弱點信息和弱點屬性進行挑選，取出一部分適合於關聯規則挖掘的弱點信息來進行實驗，同時也對量化屬性值進行了離散化處理。
  5. A new matching method of well - log and seismic data is presented so that two - dimensional distributed seismic attribute can be used in 3d reservoir geological modeling. it is named vertical equivalence method. the seismic attribute classification method based on the geological properties in reservoir geological modeling, seismic wave skewness and kurtosis conception, and the rules of seismic time window selection and attribute analysis are presented

    提出了將二維分佈的地震屬性用於三維儲層地質建模的井震數據匹配地震屬性縱向等值法、基於儲層地質建模儲層地質屬性的地震屬性分類方法、地震波形偏度與尖度的概念、地震時窗選取準則和地震屬性分析準則，為在大慶油田開展儲層地質建模建立了可選的地震屬性參數表。
  6. In order to fit in the marketing requirements of the product customization, principles has been set for the construction of product modules and the method has been presented for the classification of product modules. these achievements ensure consistency between product structure and the existing assembly situation, provide convenience for modification design in the future, and help make easier 3 dimensional modeling of new product in a acceptable period of time. all these can attribute to the rapid response to customers " needs

    2 、在產品設計開發方面，為適應個性化定製營銷的要求，明確了產品模塊的建立原則，確定了產品模塊的劃分方法，從而使整個產品與現行的裝配結構工藝保持高度的一致，又便於以後的冰箱產品的改型換代設計，並且可以為客戶盡快地提供新產品的三維模型，為企業快速地響應客戶的需求、迅速地設計出滿足需求的產品打下了堅實的基礎。
  7. This paper introduces the development of data mining and the concepts and techniques about clustering will be discussed, and also mainly discusses the algorithm of cluster based on grid - density, then the algorithm will be applied to the system of insurance ? among the various algorithms of cluster put forward, they are usually based on the concepts of distance cluster o whether it is in the sense of traditional eculid distance such as " k - means " or others o these algorithms are usually inefficient when dealing with large data sets and data sets of high dimension and different kinds of attribute o further more, the number of clusters they can find usually depends on users " input 0 but this task is often a very tough one for the user0 at the same time, different inputs will have great effect on the veracity of the cluster ' s result 0 in this paper the algorithm of cluster based on grid - density will be discussed o it gives up the concepts of distance <, it can automatically find out all clusters in that subspaceo at the same time, it performs well when dealing with high dimensional data and has good scalability when the size of the data sets increases o

    在以往提出的聚類演算法中，一般都是基於「距離（ distance ） 」聚類的概念。無論是傳統的歐氏幾何距離（ k - means ）演算法，還是其它意義上的距離演算法，這類演算法的缺點在於處理大數據集、高維數據集和不同類型屬性時往往不能奏效，而且，發現的聚類個數常常依賴于用戶指定的參數，但是，這往往對用戶來說是很難的，同時，不同參數往往會影響聚類結果的準確性。在本文里要討論的基於網格密度的聚類演算法，它拋棄了距離的概念，它的優點在於能夠自動發現存在聚類的最高維子空間；同時具有很好的處理高維數據和大數據集的數據表格的能力。
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