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数值属性的英文

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"数值属性"怎么读用"数值属性"造句

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  • numerical attribute

例句与用法

  • Tests whether all numeric properties of this
    的所有数值属性是否都具有零值。
  • Object to zero and its string properties to zero - length strings
    对象的数值属性重置为零,并将其字符串属性设置为零长度字符串。
  • Discretization : for numeric attributes , sometimes it is not useful to display each distinct value of an attribute
    离散化:对于数值属性,显示属性的每个非重复值通常毫无意义。
  • 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
    讨论了在大多数聚类算法中的相似性测量方法,并以属性的类型作为选择相似性的标准,阐述了用于数值属性,符号属性及混合属性相似性测量方法。
  • 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
    优化关联规则允许在规则中包含未初始化的属性.优化过程就是确定对这些属性进行初始化,使得某些度量最大化.最大化兴趣度因子用来发现更加有趣的规则;另一方面,允许优化规则在前提和结果中各包含一个未初始化的数值属性.对那些处理一个数值属性的算法进行直接的扩展,可以得到一个发现这种优化规则的简单算法.然而这种方法的性能很差,因此,为了改善性能,提出一种启发式方法,它发现的是近似最优的规则.在人造数据集上的实验结果表明,当优化规则包含两个数值属性时,优化兴趣度因子得到的规则比优化可信度得到的规则更有趣.在真实数据集上的实验结果表明,该算法具有近似线性的可扩展性和较好的精度
  • 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
    在数据挖掘过程中,由于利用间隔来处理数值属性容易产生尖锐的边界问题,我们引入模糊集的概念到数据挖掘算法来解决这个问题,给出了具体的算法,并通过实验证实了利用模糊关联规则和模糊频繁序列检测异常的可行性。
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