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Description: :<<数据挖掘--实用机器学习技术及java实现>>一书的配套源程序，结合数据挖掘和机器学习的知识,以java语言实现了具有代表性的各类数据挖掘方法.例如:classifier中的ZeroR.OneR.NaiveBayes.DecisionTable.IBK.C45,还有聚类,数据预处理等-: lt; Lt; Data Mining-- Practical Machine Learning Technology and java achieve gt; Gt; A matching the source, combining data mining and machine learning, the knowledge, java language to a representative of the various types of data mining. For example : the classifier ZeroR . OneR.NaiveBayes.DecisionTable.IBK.C45, clustering, data pretreatment
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Author: 黄 |
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Description: 由java开发的软件包，里面有人工智能所用的很多东东，包括神经网络，支持向量机，决策树等分类和回归分析方法，集成化软件哦！-by java development package, which has artificial intelligence used by many of the Eastern, including neural networks, support vector machines, such as decision tree classification and regression analysis, integrated software Oh!
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Author: XIAO |
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Description: own Java code. WEKA 是一个机器学习运算法则，它是为解决真实世界中的数据问题。它用java写成并且几乎可以在任何平台上运行。运算法则能够被直接应用到数据集上或者从你自己的java 码中调用。-own Java code. WEKA is a machine learning algorithms, it is to solve the real-world data. Using java can be written in almost any platform. Algorithms can be directly applied to the data collected on or from your own java code call.
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Description: 一个模拟weka的系统，输入文件格式和weka的一样，实现决策树的分析以及通过数据挖掘整理规则集合，很值得新手学习-a simulation system, the importation of files and weka, the same realization of the decision tree analysis and data mining collated by the rules set, is worth learning newcomers
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Author: 郑磊 |
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Description: 数据挖掘的一个研究方向,此书中介绍了数据挖掘的基本概念以及weka的用法-a data mining research, the book introduces the Data Mining and the basic concept of the usage weka
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Author: 无影 |
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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.
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Author: 马何坛 |
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Description: weka是机器学习和数据挖掘领域最有影响力的开源项目之一，大量实用的代码-weka is machine learning and data mining areas of the most influential open source projects, a lot of practical code
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Description: 基于Java及Mtlab联合应用的数据挖掘平台，可结合Spiker联合实现支持向量机的数据挖拙。-Based on Java and Mtlab combination of data mining platform, in conjunction with the Joint Spiker realize SVM dug Zhuo data.
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Author: 许伟 |
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Description: weka 源代码很好的 对于学习 数据挖掘算法很有帮助-weka source code good for learning data mining algorithms helpful
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Author: jiaqiangs |
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Description: 新西兰大学开发的一款数据挖掘软件，在java虚拟机环境下运行，用户可以自己编写算法程序在软件里进行验证-New Zealand University developed a data mining software in java virtual machine environment running, the user can prepare its own procedures in the software algorithm to verify
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Author: cc |
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Description: WEKA开放环境的建立，使用eclipse配置weka-WEKA establishment of an open environment, the use of eclipse configuration weka
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Author: lyh |
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Description: Weka，一个数据挖掘工具。功能包括：分类、聚类和关联规则等等。这是该开源软件的源代码，版本为3.5.7-Weka, a data mining tool. Features include: classification, clustering and association rules, etc.. This is the open source software source code, version 3.5.7
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Author: Jess |
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Description: weka全名是怀卡托智能分析环境（Waikato Environment for Knowledge Analysis），是一个公开的数据挖掘工作平台，集合了大量能承担数据挖掘任务的机器学习算法，包括对数据进行预处理，分类，回归、聚类、关联规则以及在新的交互式界面上的可视化-full name is weka intelligent analysis environment Waikato (Waikato Environment for Knowledge Analysis), is an open platform for data mining work, collection of a large number of data mining capable of undertaking the task of machine learning algorithms, including data pre-processing, classification, regression , clustering, association rules, as well as in the new interactive visualization interface
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Author: 朱磊 |
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Description: Java 编写的多种数据挖掘算法 包括聚类、分类、预处理等-Java to prepare a variety of data mining algorithms, including clustering, classification, preprocessing, etc.
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Author: 闫珍 |
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Description: Data Mining Software in Java
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Author: arra |
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Description: 数据挖掘分类算法：J48源代码，采用JAVA语言编程实现-Data mining classification algorithms: J48 source code, the use of JAVA programming language
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Author: liuchunju |
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Description: EM 算法是 Dempster，Laind，Rubin 于 1977 年提出的求参数极大似然估计的一种方法，它可以从非完整数据集中对参数进行 MLE 估计，是一种非常简单实用的学习算法。这种方法可以广泛地应用于处理缺损数据，截尾数据，带有讨厌数据等所谓的不完全数据(incomplete data)。需要weka的算法包支持。-EM algorithm is Dempster, Laind, Rubin in 1977 for the parameters proposed by maximum likelihood estimation of a method, it can focus from non-complete data MLE of the parameters estimated, is a very simple and practical learning algorithm. This method can be widely applied to deal with defect data, censored data, with data such as the so-called hate incomplete data (incomplete data).
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Author: zhangrui |
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Description: 基于局部搜索能力强、收敛速度快的特点,首先初始化一个没有子种群的全局种群,再在全局种群中采用迭代搜索,并对其中的个体进行聚类,当聚类簇中的个体数目达到规定的最小规模时形成一个子种群,然后在各子种群中进行迭代搜索并重新进行聚类,从而提高进化过程中种群的多样性,增强算法跳出局部最优的能力.该算法基于weka，用于weka拓展功能，需要 weka算法包支持。-Based on the local search ability, the characteristics of fast convergence, first initialize a sub-population of the overall population, then the overall population in the iterative search, and clustering of the individuals, when the clustering of individual cluster achieve the required minimum number of the scale of the formation of a subset of the population, and then in the sub-populations in the iterative search and re-clustering to improve the evolutionary process of population diversity, enhancement algorithm' s ability to jump out of local optimum.
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Author: zhangrui |
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Description: 基于weka的分类算法，用于weka拓展应用。朴素贝叶斯模型发源于古典数学理论，有着坚实的数学基础，以及稳定的分类效率。同时，该算法所需估计的参数很少，对缺失数据不太敏感，算法也比较简单。理论上，与其他分类方法相比具有最小的误差率。-Based on the classification algorithm weka, weka develop applications for. Naive Bayes model originated in the classical mathematical theory, has a solid mathematical basis, as well as the stability of the classification efficiency. At the same time, the algorithm estimates the parameters required for small, less sensitive to missing data, the algorithm is also relatively simple. In theory, when compared with other classification methods with the smallest margin of error.
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Size: 7168 |
Author: zhangrui |
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Description: weka源代码 最全最新的 数据挖掘用机器学习实现。包含聚类 分类 关联规则 离群点监测。java平台-weka most up-to-date source of data mining using machine learning to achieve. Clustering association rules classification contains outliers monitoring. java platform
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Author: 王某 |
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