Description: Form1.cs是应用聚类算法DBSCAN (Density-Based Spatical Clustering of Application with Noise)的示例，可以通过两个参数EPS和MinPts调节聚类。DBSCAN.cs是全部算法的实现文件，聚类算法的进一步信息请参考“数据挖掘”或者相关书籍。聚类示例数据来自于sxdb.mdb，一个Access数据库。-Form1.cs clustering algorithm is applied DBSCAN (Density-Based S patical Clustering of Application with Noise) example, the two parameters can EPS and MinPts regulation clustering. DBSCAN.cs algorithm is the realization of all documents, the clustering algorithm further information please refer to the "data mining" or books. Clustering sample data from sxdb.mdb, an Access database. Platform: |
Size: 26624 |
Author:yang |
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Description: 这是一个聚类的工具箱,可以用到数据挖掘等高级应用中去.-This is a cluster box can be used as data mining application to the High. Platform: |
Size: 62464 |
Author:胡志坤 |
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Description: 两个som聚类算法，可以用于图象处理，数据挖掘，运行感觉不错，推荐！值得收藏！-two som clustering algorithm can be used in image processing, data mining, running good feeling recommended! Worth collecting! Platform: |
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Author:刘鹏 |
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Description: 将Weka数据挖掘工具所产生的K-MEANS和DBSCAN结果转化成MATLAB可输出三维图像的格式-Weka data mining tools will be generated by K-MEANS and DBSCAN results can be exported into MATLAB three-dimensional image format Platform: |
Size: 1006592 |
Author:王坤 |
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Description: 数据挖掘算法的实现，基于模糊聚类的最大树算法，数据集是darpa99,也就是KDD-CUP99中采用的数据集-The realization of data mining algorithms, based on fuzzy clustering of the largest tree algorithm, a data set is darpa99, which is used in KDD-CUP99 data set Platform: |
Size: 32768 |
Author:谢松林 |
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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: |
Size: 1103872 |
Author:xq |
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Description: 一种非常实用的C均值聚类算法，可以用于数据挖掘、图像分割等领域-A very useful C-means clustering algorithm can be used for data mining, image segmentation and other areas of Platform: |
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Author:hanguang |
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Description: This package contains a MATLAB implementation of the cluster
visualization tool CLUSION as described in
A. Strehl and J. Ghosh, "Relationship-based Clustering and
Visualization for High-dimensional Data Mining", Special
Issue on Mining Web-based Data for E-Business Applications
of the INFORMS Journal on Computing, 2002-This package contains a MATLAB implementation of the cluster
visualization tool CLUSION as described in
A. Strehl and J. Ghosh, "Relationship-based Clustering and
Visualization for High-dimensional Data Mining", Special
Issue on Mining Web-based Data for E-Business Applications
of the INFORMS Journal on Computing, 2002 Platform: |
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Author:thirtyfive |
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Description: 模糊聚类虽然能够对数据聚类挖掘，但是由于网络入侵特征数据维数较多，不同入侵类别间的数据差别较小，不少入侵模式不能被准确分类。本案例采用结合模糊聚类和广义神经网络回归的聚类算法对入侵数据进行分类。
-Although fuzzy clustering to cluster the data mining, but the characteristics of the network intrusion data more dimensions, different invasion was little difference between categories of data, many intrusion model can not be accurately classified. This case using fuzzy clustering and generalized regression neural network clustering algorithm to classify data on the invasion. Platform: |
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Author:si |
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Description: 基于FCM的数据聚类分析及Matlab 实现
戈国华, 肖海波, 张敏
( 江西理工大学信息工程学院江西赣州341000 )
【摘要】: 聚类是数据挖掘中常用的数据分析技术。本文详细介绍了FCM聚类算法的理论和实现步骤。并用Matlab 演
示了FCM用于数据聚类。结果表明FCM算法是一种高效的数据聚类算法, 有很广泛的应用。-FCM clustering analysis based on the data and Matlab implementation Ge Guohua, Xiao Haibo, Zhang Min (Institute of Information Engineering, Jiangxi University of Ganzhou, Jiangxi 341000, China) Abstract: Clustering is commonly used in data mining, data analysis techniques. This paper describes the FCM clustering algorithm theory and implementation steps. And demonstrates the use of Matlab for data clustering FCM. The results show that the FCM algorithm is an efficient data clustering algorithm, a very wide range of applications. Platform: |
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Author:yueyue |
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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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