numeric attribute 中文意思是什麼
numeric attribute
解釋
數字屬性-
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演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演算法.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，該演算法具有近似線性的可擴展性和較好的精度 -
In the implementation of data classifier, we describe extraction and management of conceptual hierarchy for data, also design an automatic extraction algorithm for numeric data. in this section, we still provide the two algorithms of concept - based attribute - oriented induction and evaluating classification scheme and the visualization of classification rule. finally, the data classifier is tested in databas the results show that it is practical and its performance meet the requirement of designing
然後，在數據分類器的實現中，論述了數據的概念層次提取和管理，並對數值型數據給出了一個自動提取概念層次演算法；同時給出了基於面向屬性歸納的分類演算法、分類模式的評價演算法和分類規則的可視化方法。 -
Attribute to a positive numeric value, and set the
屬性設置為一個正數值，將 -
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。 -
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。 -
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
本文首先分析了串列sliq演算法的原理和特點，針對其不足提出了一些改進方法，然後在基於pvm的環境下實現了演算法的并行化，分析了演算法的時間復雜度和加速比，提高了sliq演算法的效率，具有一定的理論意義和實用價值。 -
Enumerations and numeric ranges : for example, the value of a particular string attribute must be a, c, or d. another example : the value of a particular numeric must be between 1 and 500
枚舉與數字范圍：例如，某個特定字元串屬性必須為a 、 c或者d 。再比如，某個特定的數字必須在1至500之間。 -
Firstly, some basic algorithms for inducing decision tree are discussed, including id3, which uses information gain to select a splitting attribute when partitioning a training set ; c4. 5, which can deal with numeric attributes ; cart, which uses gini rule in attribute selection and induces a binary tree ; public, which puts tree pruning in the tree building phase ; interactive method, which puts artificial intelligence and human - computer interaction into the procedure of decision tree induction ; as well as sliq and sprint which are scalable and can be easily parallelized. advantages and disadvantages of these algorithms are also presented
文中詳細闡述了幾種極具代表性的決策樹演算法：包括使用信息熵原理分割樣本集的id3演算法；可以處理連續屬性和屬性值空缺樣本的c4 . 5演算法；依據gini系數尋找最佳分割並生成二叉決策樹的cart演算法；將樹剪枝融入到建樹過程中的public演算法；在決策樹生成過程中加入人工智慧和人為干預的基於人機交互的決策樹生成方法；以及突破主存容量限制，具有良好的伸縮性和并行性的sliq和sprint演算法。
分享友人