Description: id3算法进行决策树生成 以信息增益最大的属性作为分类属性，生成决策树，从而得出决策规则。id3的源码决策树最全面最经典的版本.id3决策树的实现及其测试数据.id3 一个有用的数据挖掘算法，想必对大家会有所帮助！-id3 decision tree algorithms to generate information gain the greatest attribute as a classification attributes, generate decision tree, Thus, it reached a decision-making rules. Id3 the most comprehensive source Decision Tree classic version. Id3 decision tree and the achievement test data. Id3 1 useful data mining algorithms, surely they will help you! Platform: |
Size: 11293 |
Author:李顺古 |
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Description: id3的源码决策树最全面最经典的版本.id3决策树的实现及其测试数据.id3 一个有用的数据挖掘算法，想必对大家会有所帮助！id3算法进行决策树生成 以信息增益最大的属性作为分类属性，生成决策树，从而得出决策规则。-id3 the most comprehensive source Decision Tree classic version. Id3 decision tree and the achievement test data. I d3 a useful data mining algorithms, surely they will help you! Id3 decision tree algorithms to generate information gain the greatest attribute as a classification attributes, generate decision tree, Thus, it reached a decision-making rules. Platform: |
Size: 40058 |
Author:王小明 |
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Description: id3算法进行决策树生成 以信息增益最大的属性作为分类属性，生成决策树，从而得出决策规则。id3的源码决策树最全面最经典的版本.id3决策树的实现及其测试数据.id3 一个有用的数据挖掘算法，想必对大家会有所帮助！-id3 decision tree algorithms to generate information gain the greatest attribute as a classification attributes, generate decision tree, Thus, it reached a decision-making rules. Id3 the most comprehensive source Decision Tree classic version. Id3 decision tree and the achievement test data. Id3 1 useful data mining algorithms, surely they will help you! Platform: |
Size: 38912 |
Author:李顺古 |
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Description: id3的源码决策树最全面最经典的版本.id3决策树的实现及其测试数据.id3 一个有用的数据挖掘算法，想必对大家会有所帮助！id3算法进行决策树生成 以信息增益最大的属性作为分类属性，生成决策树，从而得出决策规则。-id3 the most comprehensive source Decision Tree classic version. Id3 decision tree and the achievement test data. I d3 a useful data mining algorithms, surely they will help you! Id3 decision tree algorithms to generate information gain the greatest attribute as a classification attributes, generate decision tree, Thus, it reached a decision-making rules. Platform: |
Size: 39936 |
Author:王小明 |
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Description: 该代码是数据挖掘里面的决策树算法 利用ID3理论，通过对训练数据的分析判断，计算出各个数据的其它对目标属性的重要程度，即计算出每个其它数据的信息增益值来将训练数据逐步分类，最后得出目标分类，从而实现决策树的生成过程。最后即可利用此决策树来对新的数据进行测试，判断其目标属性的可能值。-The data mining code is inside the ID3 decision tree algorithm using the theory of training data by analyzing the data to calculate all the other attributes on the target level of importance, that is, every other data to calculate the information gain value to the training data Category gradually came to the conclusion that the target classification, in order to achieve the process of decision tree generation. Finally, you can use this decision tree to test the new data to determine its objectives may be the value of properties. Platform: |
Size: 289792 |
Author: |
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Description: 该代码是数据挖掘里面的决策树算法 利用c45理论，通过对训练数据的分析判断，计算出各个数据的其它对目标属性的重要程度，即计算出每个其它数据的信息增益值来将训练数据逐步分类，最后得出目标分类，从而实现决策树的生成过程。最后即可利用此决策树来对新的数据进行测试，判断其目标属性的可能值。-The code is a data mining using decision tree algorithm inside the C45 theory, through the analysis of training data to calculate all the other data attributes on the target level of importance, that is, every other data to calculate the information gain value of the training data Category gradually came to the conclusion that the target classification, in order to realize the process of decision tree generation. Finally you can use this decision tree to test the new data to determine its target attributes possible values. Platform: |
Size: 769024 |
Author:zkm |
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Description: ID3决策树程序，内含训练和测试数据。 输入为选择原训练数据集和测试数据集的百分数，如0.25。根据实验要求，测试数据都选全部，故都输入1。
输出结果为实验要求的实验数据结果。-ID3 decision tree procedures, including training and test data. Input for the selection of the original training data set and test data set percentage, such as 0.25. According to the experimental requirements, the test data are the election of all, ancient capital input 1. Output requirements for the experimental results of the experimental data. Platform: |
Size: 187392 |
Author:高峰 |
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Description: D3的源码决策树最全面最经典的版本.id3决策树的实现及其测试数据.id3 一个有用的数据挖掘算法，想必对大家会有所帮助！id3算法进行决策树生成 以信息增益最大的属性作为分类属性，生成决策树，从而得出决策规则。-D3 of the source tree the most comprehensive version of the most classic. Id3 decision tree and its test data. Id3 a useful data mining algorithms, will be helpful to everyone! id3 decision tree algorithm to generate the greatest attributes of information gain as a classification attribute, to generate decision tree so as to arrive at the decision-making rules. Platform: |
Size: 39936 |
Author:kintsen |
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Description: 决策树ID3算法,带详细注释,可参考《数据挖掘概念与技术》里P185~P196,以MyEclipse7.0环境下开发的
测试数据在data文件下-Decision Tree ID3 Algorithm with extremely detail comments, refering to the book of Data Minning Concepts and Techniques. Do comprehend the content from Page 185 to Page 196 in this book.
Developed in MyEclipse7.0
Test data lies in folder data.
good luck Platform: |
Size: 80896 |
Author:黑伯爵 |
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Description: 有关人工智能，数据挖掘中的决策树算法的实现包括相关的测试数据-On artificial intelligence, data mining, decision tree algorithm in the implementation, including relevant test data Platform: |
Size: 3691520 |
Author:常 |
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Description: 数据挖掘课中的一个算法c4.5决策树源码，只要有相应的测试文件就可以了-Data mining courses in a decision tree algorithm c4.5 source code, as long as the corresponding test file on it Platform: |
Size: 140288 |
Author:王俊杰 |
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Description: 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 possible tests that can split the data set and selects a test that gives the best information gain. For each discrete attribute, one test with outcomes as many as the number of distinct values of the attribute is considered. For each continuous attribute, binary tests involving every distinct values of the attribute are considered. In order to gather the entropy gain of all these binary tests efficiently, the training data set belonging to the node in consideration is sorted for the values of the continuous attribute and the entropy gains of the binary cut based on each distinct values are calculated in one scan of the sorted data. This process is repeated for each continuous attributes. Platform: |
Size: 2048 |
Author:rajesh |
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Description: 数据挖掘的应用，对数据集建立决策树，然后利用测试集中的数据判断测试。并给出最终的决策判断的正确率。-The application of data mining, decision tree on the data set, and then use the test to determine the test data set. The final decision making given the correct rate. Platform: |
Size: 7168 |
Author: |
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Description: 决策树，ID3算法，供大家参考学习，相互交流，相互帮助，共同进步-Decision Tree ID3 Algorithm with extremely detail comments, refering to the book of Data Minning Concepts and Techniques. Do comprehend the content from Page 185 to Page 196 in this book. Developed in MyEclipse7.0 Test data lies in folder data. good luck Platform: |
Size: 15360 |
Author:liuying |
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Description: 该程序用VC++6.0编写决策树，包括训练程序，和决策程序，和训练以及测试所用的数据-The program written with VC++6.0 decision tree, including training procedures, and decision-making procedures, and training and test data used Platform: |
Size: 1726464 |
Author:wangyuxia |
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Description: Bayes分类器——算法设计
1． 使用决策树(Decision tree)分类算法、朴素贝叶斯（Naï ve Bayes）算法或者K-近邻（kNN）算法（三者任选其一）对给定的训练数据集构造分类器，并在测试数据集上进行分类预测。
2． 数据集描述：
Tic-tac-toe游戏的二叉分类。Tic-tac-toe游戏示例如下-Bayes classifier- Algorithm 1. Using the decision tree (Decision tree) classification algorithm, Naive Bayes (Naï ve Bayes) algorithm or K-nearest neighbor (kNN) algorithm (choose any one of three) on a given set of training data classification structure, and the test data Classification and Prediction on the set. 2. Data set description: Tic-tac-toe game binary classification. Tic-tac-toe game example is as follows Platform: |
Size: 1439744 |
Author:vera |
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Description: matlab实现决策树C4.5算法,首先利用训练数据创建决策树，再用测试数据对决策树进行剪枝。-C4.5 decision tree algorithm matlab realize, first use training data to create decision trees, and then test data for decision tree pruning. Platform: |
Size: 2048 |
Author:王杰 |
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Description: 随机森林算法的构造过程：1、通过给定的原始数据，选出其中部分数据进行决策树的构造，数据选取是”有放回“的过程，我在这里用的是CART分类回归树。
2、随机森林构造完成之后，给定一组测试数据，使得每个分类器对其结果分类进行评估，最后取评估结果的众数最为最终结果-Random Forest algorithm construction process: 1, by a given raw data, which part of the decision tree data structure, data selected is " back" process, I am here with the CART classification and regression trees. 2, after the completion of the random forest structure, given a set of test data, so that each classifier to uate the results of its classification, the number of all the final results of the assessment to take most of the final results Platform: |
Size: 9216 |
Author:小代 |
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Description: 用MATLAB实现的决策树，附带有测试代码和数据集。-MATLAB implementation of a decision tree, comes with test code and data sets. Platform: |
Size: 2048 |
Author:郝韵致 |
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Search - decision tree test data