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quantitative attribute in Chinese

Pronunciation:
How to pronounce "quantitative attribute""quantitative attribute" in a sentence

Translationmobile phoneMobile

  • 量的屬性
  • 品質(zhì)屬性
  • 數(shù)量屬性

Examples

  • Attributes reduct , mining classification rules , and discretizing values of quantitative attributes are three fields in mining classification rules
    屬性約簡、分類規(guī)則提取、數(shù)量屬性離散化是分類規(guī)則挖掘的三個方面。
  • ( 3 ) the evolutionary computation - based algorithm for discretizing values of quantitative attributes , whose advantage is that it can finding the best cuts of a quantitative attribute
    ( 3 )根據(jù)進化計算的極強魯棒性及尋優(yōu)能力，提出了基于進化計算的數(shù)量型屬性離散化算法?進化c均值算法。
  • How to handle these attributes affects the mining results and the efficiency . the dissertation proposes the methods of changing the quantitative attributes into bool attributes , so that many algorithms can be used , based on enhanced fcm and the genetic method
    （ 2 ）在分布式挖掘過程中，分布式數(shù)據(jù)庫中存在著大量的數(shù)量屬性，這些數(shù)量屬性的處理好壞，直接影響著關聯(lián)規(guī)則的挖掘效果和效率。
  • And also this taxonomy gives the quantitative attributes in more sides on finer layers and emphasizes the influences of vulnerabilities on the confidentiality , authentication and availability of systems so that it can provide abundant information for vulnerability description and assessment
    此外,也提供了弱點多側(cè)面細層次上的量化屬性,并且突出了弱點對系統(tǒng)機密性、可鑒別性和可用性等安全屬性的影響,為量化的弱點描述、評估提供了豐富的信息支持。
  • However , in practical applications , considering cost in constructing the networks or management in networks , users prefer staring networks to net networks which do not meet their requirements . the dissertation proposes the c - dma algorithm to solve this problem , based on fdm and cd . experimental results show that the performance of the c - dma is available and extendable . ( 2 ) in the process of mining association rules , the quantitative attributes exit in databases
    但是，在實際的網(wǎng)絡應用環(huán)境中，用戶基于成本和管理等方面的需要，使用的網(wǎng)絡結(jié)構往往是星型結(jié)構的，所以cd算法和fdm算法在網(wǎng)絡結(jié)構和實際的網(wǎng)絡結(jié)構不相適應，本文在cd算法及fdm算法的基礎上提出以中心結(jié)點結(jié)構的分布式關聯(lián)規(guī)則挖掘算法，并且從算法分析和仿真試驗兩個方面證明了算法的有效性和可擴展性。
  • For the data with quantitative attribute , several unsupervised discretization methods of continuous features are discussed . three simple boolean discretization methods ( equal width , equal frequency and cluster ) and fuzzy discretization are simulated based on a real population statistical database
    隨后針對數(shù)據(jù)庫中的模擬量屬性，分析了非監(jiān)督定量屬性離散化的幾種方法，在一個統(tǒng)計數(shù)據(jù)庫基礎上仿真研究了分別基于等寬、等頻和聚類的布爾型分段離散化方法和模糊離散化方法。
  • 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(shù)據(jù)挖掘過程中,由于利用間隔來處理數(shù)值屬性容易產(chǎn)生尖銳的邊界問題,我們引入模糊集的概念到數(shù)據(jù)挖掘算法來解決這個問題,給出了具體的算法,并通過實驗證實了利用模糊關聯(lián)規(guī)則和模糊頻繁序列檢測異常的可行性。
  • Because the existing vulnerability taxonomies at large lack support for the quantitative assessment and uncovering the vulnerability correlations , this dissertation proposes a vulnerability taxonomy for mining the correlations with multidimensional quantitative attributes , gives the corresponding mining algorithm and an experiment is given to validate the effectivity of the algorithm
    由于已有的弱點分類法普遍缺乏對量化評估以及對揭示弱點關聯(lián)特性的支持,為此,本文提出了一種支持關聯(lián)性挖掘的多維量化屬性弱點分類法,給出了相應的挖掘算法,并以實驗驗證了該算法的有效性。

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