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: |
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Author:马何坛 |
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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: 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 Platform: |
Size: 4790272 |
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 Platform: |
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Author:朱磊 |
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Description: 经典的数据挖掘算法的源代码，包括分类、聚类、关联规则等，非常有用。-Classical data mining algorithms of source code, including classification, clustering, association rules and so on, very useful. Platform: |
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Author:xq |
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Description: Data miner 1.0
can handle classification, association, clustering and linear regression. General inputs are mySQL tables Platform: |
Size: 145408 |
Author:Ecko |
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Description: 开发环境：eclipse
WEKA是一个数据挖掘工作平台，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。 -Development environment: eclipse WEKA is a data mining work platform, a collection of a lot to take on the task of data mining machine learning algorithms, including data pre-processing, classification, regression, clustering, association rules, as well as in the new interactive interface on the visualization. Platform: |
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Author:sunwei |
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Description: Weka是一个超强功能的machine learning开发包-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. Platform: |
Size: 18667520 |
Author:Alan |
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Description: 本书是一本全面介绍数据挖掘和知识发现技术的专业书籍，它系统地阐述了数据挖掘和知识发现技术的产生、发展、应用以及相关概念、原理和算法，对数据挖掘中的主要技术分支，包括关联规则、分类、聚类、序列、空间以及web挖掘等进行了理沦剖析和算法描述。本书的许多内容是作者们在攻读博士学位期间的工作总结，一方面，对于相关概念和技术的阐述尽量先从理论分析人手，在此基础上进行技术归纳；另一方面，为了保证技术的系统性，所有的挖掘模型和算法描述都在统一的技术归纳框架下进行。同时，为了避免抽象算法描述给读者带来的理解困难，本书的所有典型算法都通过具体跟踪执行实例来进一步说明。-This book is a comprehensive description of data mining and knowledge discovery technology, professional books, which systematically describes data mining and knowledge discovery technology generation, development, application and related concepts, principles and algorithms for data mining in the branch of the main techniques , including association rules, classification, clustering, sequence, space and web mining conducted a reasonable analysis and algorithms described perish. Much of this book is the authors stated in their summary of the work during a PhD, on the one hand, for the elaboration of related concepts and techniques starting with theoretical analysis as manpower, in this based on technical induction the other hand, in order to ensure technology systematic, all the mining models and algorithms described are summarized in a unified technology framework. Meanwhile, in order to avoid the abstract description of the algorithm to the readers understanding of the difficulties, Platform: |
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Author:小颖 |
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Description: Rose2里含有好多粗糙集的算法，可以实现数据预处理约简，求关联规则，求聚类和分类，非常实用。-Rose2 contains a lot of rough set algorithm, can achieve reduction of data preprocessing, find association rules, clustering and classification requirements, very practical. Platform: |
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Author:曹盛文 |
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Description: 数据挖掘 《机器学习与数据挖掘：方法与应用》，朱明等译，电子工业出版社-data mining conclude Classification,Estimation,Prediction,Affinity grouping or association rules,Clustering,Description and Visualization,Text, Web ,et al Platform: |
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Author:李杰 |
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Description: 基于K-means聚类算法的社团发现方法
先定义了网络中节点关联度,并构建了节点关联度矩阵, 在此基础上给出了一种基于 K-means聚类算法的复杂网络社团发现方法。
以最小关联度原则选取新的聚类中心, 以最大关联度原则进行模式归类,直到所有的节点都划分完为止, 最后根据模块度来确定理想的社团数-K-means clustering algorithm based on the association discovery
To define a network node correlation, and build the node correlation matrix in this basis, given a K-means clustering algorithm based on a complex network of associations that way.
The principle of the minimum correlation to select a new cluster center to the principle of maximum correlation pattern classification until all the nodes are divided until the end, the last under the module to determine the degree of the ideal number of community Platform: |
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Author:maverick |
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Description: 主要介绍在大型数据库中发现知识（Knowledge Discovery in Large Databases, KDD）的各种技术，是专门针对决策支持中的各类问题进行讨论的高端课程。面向对象为软件工程专业硕士研究生。
本课程讲授的主要内容包括：数据预处理、数据仓库及OLAP、概念描述型数据挖掘、关联规则挖掘、分类挖掘和预测以及聚类挖掘，涉及的领域包括数理统计、概率论、机器学习、信息论、集合论等等。-Introduces knowledge discovery in large databases (Knowledge Discovery in Large Databases, of KDD), a variety of technology, specifically for decision support in all kinds of problems discussed in high-end courses. Object-oriented software engineering graduate. This course will include: data preprocessing, data warehousing and OLAP, the concept of descriptive data mining, association rule mining, classification mining and prediction, and clustering mining in the areas of mathematical statistics, probability theory, machine learning, information theory , set theory, and so on. Platform: |
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Author:onlearning |
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Description: 数据挖掘(Data Mining)阶段首先要确定挖掘的任务或目的。数据挖掘的目的就是得出隐藏在数据中的有价值的信息。数据挖掘是一门涉及面很广的交叉学科，包括器学习、数理统计、神经网络、数据库、模式识别、粗糙集、模糊数学等相关技术。它也常被称为“知识发现”。知识发现(KDD)被认为是从数据中发现有用知识的整个过程。数据挖掘被认为是KDD过程中的一个特定步骤，它用专门算法从数据中抽取模式(patter，如数据分类、聚类、关联规则发现或序列模式发现等。数据挖掘主要步骤是：数据准备、数据挖掘、结果的解释评估。-Data Mining (Data Mining) stage must first determine the mission or purpose of the excavation. The purpose of data mining is to draw valuable information hidden in the data. Data mining is an interdisciplinary involving a wide range, including control study, mathematical statistics, neural networks, databases, pattern recognition, rough sets, fuzzy mathematics and other related technologies. It is also often referred to as the " knowledge discovery" . Knowledge discovery (KDD) is that the whole process is to discover useful knowledge from data. Data mining is a particular step in the KDD process, with a special algorithm (patter, such as data classification, clustering, association rules discovery or sequential pattern discovery. Extracted from the data model and data mining major steps: data preparation, data mining, interpretation of the results evaluated. Platform: |
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Author:dlufl |
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Description: Apriori 数据挖掘算法的C#实现
数据库中的知识发现 (Knowledge Discovery in Databases,KDD) 是利用计算机自动地从海量信息中提取有用的知识 , 是一种有效利用信息的新方法 , 目前已成为数据库领域的研究热点之一。 KDD 的研究焦点在于数据挖掘。数据挖掘是从大型数据库或数据仓库中提取人们感兴趣的知识 , 这些知识是隐含的 , 事先未知的潜在的有用信息。主要包括的方法有 : 分类、回归分析、聚类、关联分析等 [1][5] 。关联规则的提取主要针对大型事务数据库。由于关联规则提取需要重复扫描数据库 , 因而提高算法的效率是至关重要的。
-Apriori data mining algorithms C# knowledge discovery in databases (Knowledge Discovery in Databases, KDD) is using the computer to automatically extract useful knowledge from the mass of information is an effective use of the new method of information has become the database field research focus. KDD research focuses on data mining. Data mining is extracted from a large database or data warehouse people are interested in knowledge, such knowledge is implicit, previously unknown potentially useful information. Mainly include: classification, regression analysis, clustering, association analysis [1] [5]. The extraction of association rules mainly for large transaction database. Association rules extraction need to repeat the scan database, and therefore it is essential to improve the efficiency of the algorithm. Platform: |
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Author:王浩臣 |
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Description: weka入门学习中用到的银行数据，里面有600个实例，用于分类、聚类、回归或这关联规则 初入门试用。-the weka Getting started learning to use the bank data, there are 600 instances for classification, clustering, regression, association rules early entry to the trial. Platform: |
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Author:zjl |
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Description: DIGGER社交网络数据挖掘分析系统，本系统的挖掘工作是在WEKA平台下进行的，WEKA作为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。DIGGER作为一个社交网络系统，它利用数据挖掘工具WEKA在大规模海量数据中建立模型和发现数据间关系，这些模型和关系可以用来做出决策和预测。
第一章描述了开题的背景和需求，第二章描述该系统的关键技术和开发环境，第三章是系统的设计，第四章对框架的研究和系统中框架的运用，第五章是对系统安全性的研究以及在系统中的实现，第六章对系统进行测试。第七章就本次的设计进行了总结。-DIGGER social network data mining analysis system, the system of the excavation work was carried out in the WEKA platform, WEKA data mining as an open working platform, a collection of a lot to take on the task of data mining machine learning algorithms, including preprocessing of the data , classification, regression, clustering, association rules, and in the new interactive interface visualization. DIGGER as a social networking system that uses data mining tool WEKA in large amounts of data to establish the relationship between the data model and the discovery of these models and relationships can be used to make decisions and predictions. The first chapter describes the background and needs of the opening title, the second chapter describes the key technologies of the system and development environment, and the third chapter is the system design, the fourth chapter of the framework of research and the use of the framework of the system, the fifth chapter is Research on system securi Platform: |
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Author:萦婧青心 |
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Description: 遥感影像数据挖掘是一个有着广阔应用前景的研究领域。对图像检索、图像分类、图像聚类、空间 关联规则挖掘和图像变化检测等数据挖掘应用而言，相似性度量是基础和前提。采用了图像空间划分的策略，在此基础上计算颜色、纹理和形状等3方面的低层视觉 特征来描述图像，采用多维特征空间的网格划分来降低数据维数并建立了影像的相似性度量。实验结果表明，该方法对影像具有一定的几何和光照不变性。-Remote sensing data mining is a promising research has broad application fields. Image retri , image classification, image clustering, association rules mining and spatial image change detection, data mining applications, the similarity measure is the basis and premise. Using the image space division strategy, calculate the low-level visual features as color, texture and shape in three areas on this basis, to describe the image, using the mesh of multidimensional space to reduce the dimensionality of the data and to establish a similarity measure image . Experimental results show that this method has a certain image geometry and illumination invariance. Platform: |
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Author:fangsm |
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Search - classification clustering association