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numeric attribute中文是什么意思

  • 數字屬性

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  • 例句與用法
  • 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。
  • 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
    默認大小是6 ,可以通過將數字屬性添加到web . config文件中的標記來更改此大小,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
    為了進一步提高分類準確率和速度,論文對sliq算法作了一些改進:用新的屬性選擇度量代替gini索引,用處理連續值屬性的方法處理只有兩個可能值的分類屬性。
  • 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
    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,該算法具有近似線性的可擴展性和較好的精度
  • Followed by the rapid extension of data size , the usage of parallel technology is a very important method to improve the efficiency of data ming . sliq uses novel pre - sorting and breadth - first techniques to build a decision tree fast and accurately on a large data set , and can deal both categorical and numeric attributes . but the primary algorithm contains the abundant computing on attribute and record
    本文首先分析了串行sliq算法的原理和特點,針對其不足提出了一些改進方法,然后在基于pvm的環境下實現了算法的并行化,分析了算法的時間復雜度和加速比,提高了sliq算法的效率,具有一定的理論意義和實用價值。
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