Description: 本代码是用java语言编写的基于决策树c4.5算法的数据挖掘程序，它可以在很多领域如股票系统中使用 -the code is written in java-based Decision Tree Algorithm Bank data mining process, it can in many areas such as the use of the stock system Platform: |
Size: 231424 |
Author:飞翔 |
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Description: c45决策树改进算法，主要用于数据挖掘中的聚类分析。对从事dm研究的人应该有用-C45 Decision Tree Algorithm, mainly for data mining of cluster analysis. Dm engaged in research should be useful Platform: |
Size: 1024 |
Author:仲伟宽 |
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Description: 这是决策树C4.5算法的java版源码。希望大家能喜欢，愿共同分享！-Decision Tree Algorithm C4.5 java version of the source code. Hope you will like it is willing to share! Platform: |
Size: 1024 |
Author:gosiger |
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Description: Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes.
一个可以实现多种方法分类的软件，利用各个
对象的属性。决策树，距离、密度等-Weka is a collection of machine learning al gorithms for data mining tasks. The algorithms can either be applied directly to a dataset or ca lled from your own Java code. Weka contains tool 's for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for d eveloping new machine learning schemes. can be a real Categories are various methods of software, using all the attributes of objects. Decision Tree, distance, density, etc. Platform: |
Size: 15446016 |
Author:马何坛 |
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Description: 实现决策树分类训练试验。
源自c4.5，在windows下用C++实现,简洁好用。用户只需要构建好特征说明文件，并选择一些参数既可以进行试验。-Realize decision tree classifier trained pilot. From c4.5, in windows using C++ Realize, concise easy to use. Users only need to build a good feature of the documentation, and select some of the parameters can be tested. Platform: |
Size: 139264 |
Author:胡伟湘 |
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Description: 决策树在Weka中的设计与实现,数据的属性要是名词性的。-Weka decision tree in the design and realization of the attributes of data if the term in nature. Platform: |
Size: 3072 |
Author:snow |
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Description: C4.5算法有如下优点：产生的分类规则易于理解，准确率较高。其缺点是：在构造树的过程中，需要对数据集进行多次的顺序扫描和排序，因而导致算法的低效。此外，C4.5只适合于能够驻留于内存的数据集，当训练集大得无法在内存容纳时程序无法运行。-C4.5 algorithm has the following advantages: the classification rules easier to understand, accurate and a higher rate. Its shortcomings are as follows: in the tree structure, the need for a number of data sets the order of scanning and sorting, thus leading to inefficient algorithms. In addition, C4.5 can only be applied to the presence of a data set in memory, when the training set too great to accommodate in memory when the program can not run. Platform: |
Size: 1024 |
Author:xinyuanwo |
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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决策树算法的JAVA实现：ID3算法是机器学习中的一种分类方法，本例子用java构建多叉树来实现id3算法。-ID3 Decision Tree Algorithm JAVA realize: ID3 machine learning algorithm is a classification method, the example of using java to build a multi-tree algorithm id3 realize. Platform: |
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Author:more |
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Description: Many of the pattern fi nding algorithms such as decision tree, classifi cation rules and clustering
techniques that are frequently used in data mining have been developed in machine learning
research community. Frequent pattern and association rule mining is one of the few excep-
tions to this tradition. The introduction of this technique boosted data mining research and its
impact is tremendous. The algorithm is quite simple and easy to implement. Experimenting
with Apriori-like algorithm is the fi rst thing that data miners try to do. Platform: |
Size: 132096 |
Author:鱼彬彬 |
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Description: this an weka tool source code implemented in java used for decision tr-this is an weka tool source code implemented in java used for decision tree Platform: |
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Author:grandhe |
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Description: A Decision Tree program implemented in Java, which have to divide data into a Binary tree structure Platform: |
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Author:Shehzad |
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Description: 决策树算法ID3的代码实验，编译后可直接使用的ID3代码-ID3 decision tree algorithm code experiments, compiled code can be directly used in ID3 Platform: |
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Author:xiaolin |
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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: 用java 语言编写的人工智能中的决策树演示-Using java language of artificial intelligence in the decision tree presentation Platform: |
Size: 418816 |
Author:杨柳 |
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