numeric attribute meaning in Chinese
数字属性
Examples
- 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。 - The default size is 6 , and can be changed by adding a numeric attribute to a tag in the web . config file , as shown in the following example
默认大小是6 ，可以通过将数字属性添加到web . config文件中的标记来更改此大小，如下面的示例所示。 - For the sake of further improving performance , this paper made some improvements to sliq . first , we use a new splitting index to evaluate the “ goodness ” of the alternative splits for attributes instead of gini index . secondly , we regard categorical attributes with only two possible values as numeric attributes when evaluate splits
为了进一步提高分类准确率和速度,论文对sliq算法作了一些改进:用新的属性选择度量代替gini索引,用处理连续值属性的方法处理只有两个可能值的分类属性。 - 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