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C4.5的简化版本是一个决策树的算法-C4.5 is a simplified version of the Decision Tree Algorithm
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One kind of decision-making tree algorithm, can be seen as one kind data mining algorithm ,find the rule from large-scale data middle.
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决策树分类算法，c4.5，java语言进行描述的-Decision tree classification algorithm, c4.5, java language described
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his algorithm was proposed by Quinlan (1993). The C4.5 algorithm generates a classification-decision tree for the given data-set by recursive partitioning of data. The decision is grown using Depth-first strategy. The algorithm considers all the poss
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实现ID3算法，在结果中以树表示出来。决策树是对数据进行分类，以此达到预测的目的。该决策树方法先根据训练集数据形成决策树，如果该树不能对所有对象给出正确的分类，那么选择一些例外加入到训练集数据中，重复该过程一直到形成正确的决策集。-ID3 algorithm to achieve, in the results that come out to the tree. Decision tree is to classify the data, thus achieving the purpose
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数据挖掘决策树ID3算法原理 适用数据挖掘的模拟测试-ID3 decision tree data mining applies data mining algorithm simulation tests
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商业只能中数据挖掘的决策树算法 用于数据分类-Data Mining Decision Tree Algorithm
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here is the decision tree in classification algorithm which is build in excel file.
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algorithm of decision tr-algorithm of decision tree
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decision tree algorithm
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关于数据挖掘的分类算法，主要介绍基于不确定数据的决策树算法-With regard to the classification of data mining algorithms, mainly introduces the decision tree algorithm based on uncertain data
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A java based open source data mining code for decision tree algorithm
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decision tree algorithm
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决策树算法的研究与改进,
决策树算法的研究与改进-Decision Tree Research and Improvement, and Improved Decision Tree Algorithm
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its a tutorial for using WEKA J48 decision tree algorithm . the document language is persian
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使用MATLAB语言完成的决策树算法。
里面有详细说明-Using the MATLAB language to complete the decision tree algorithm. Details inside
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是一个决策树算法，该算法-Is a decision tree algorithm
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决策树算法是利用最广泛的 数据挖掘算法之一-The decision tree algorithm is the most widely used data mining algorithms, one of...
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基于ID3理论的二叉树决策树算法的C++实现-Implemented decision tree algorithm, based on the the ID3 theory of binary tree C++
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关于决策树C4.5算法的几篇学术论文（基于C4.5算法的遥感影像分类）-Several papers (C4.5 decision tree algorithm C4.5 algorithm based on image classification)
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