Description: 关于聚类有效性函数FP的研究，最后结果显示不适用。-function of the FP clustering validity of the final result showed not apply. Platform: |
Size: 36253 |
Author:石支柱 |
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Description: 关于聚类有效性函数FP的研究，最后结果显示不适用。-function of the FP clustering validity of the final result showed not apply. Platform: |
Size: 35840 |
Author:石支柱 |
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Description: K-MEANS聚类算法，以及PSO和QPSO算法改进K-MEANS算法，breastcancer数据验证了该分类模型的有效性-K-MEANS clustering algorithm, and the PSO algorithm and QPSO to improve K-MEANS algorithm, breastcancer data verified the validity of the classification model Platform: |
Size: 43008 |
Author:李慧 |
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Description:
使用matlab实现的各种聚类算法,其中包括具体例子进行详细说明。-The purpose of the development of this toolbox was to compile a continuously extensible, standard tool, which is useful for any MATLAB user for one s aim. In Chapter 1 of the downloadable related documentation one can find a theoretical introduction containing the theory of the algorithms, the definition of the validity measures and the tools of visualization, which help to understand the programmed MATLAB files.
Chapter 2 deals with the exposition of the
files and the description of the particular algorithms, and they are illustrated with simple examples, while in Chapter 3 the whole
Toolbox is tested on real data sets during the solution of three clustering problems: comparison and selection of algorithms estimating the optimal number of clusters and examining
multidimensional data sets.
About the Toolbox
The Fuzzy Clustering and Data Analysis Toolbox is a collection of MATLAB functions. The toolbox provides five categories of functions:
- Cluste Platform: |
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Author:JinJun |
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Description: CVAP includes 4 External validity indices, 14 Internal validity indices and 5 clustering algorithms (K-means, PAM, hierarchical clustering, SOM and etc.). It supports other clustering algorithms via loading a solution file with class labels, or by adding new codes. And similarity metrics of Euclidean distance and Pearson correlation coefficient are supported.-CVAP includes 4 External validity indices, 14 Internal validity indices and 5 clustering algorithms (K-means, PAM, hierarchical clustering, SOM and etc.). It supports other clustering algorithms via loading a solution file with class labels, or by adding new codes. And similarity metrics of Euclidean distance and Pearson correlation coefficient are supported. Platform: |
Size: 258048 |
Author:tra ba huy |
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Description: kmeans均值聚类算法:一种改进的基于半监督聚类的入侵检测算法ASCID（Active-learning Semi-supervised Clustering Intrusion Detection），-kmeans clustering algorithm
Algorithm was simulated by KDD 99 datasets， which the experimental results demonstrate that ASCID algorithm can improve the detection rates and low the false positive rates of the algorithm, and confirm the feasibility and validity of the algorithm. Platform: |
Size: 50176 |
Author:huhan |
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Description: 基于WEKA平台的文本聚类及实现，以及常用的文本聚类效果评价指标-Text clustering based on WEKA platform and implementation, as well as common text clustering validity Platform: |
Size: 768000 |
Author:王三 |
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Description: 在原始的fcm算法基础上，对算法中的聚类数c和加权指数m给出优选方法，进而而出了fcm参数优选自适应算法，通过人造数据与具有实际背景的数据验证可以看出该算法是有效的，该算法不但可以自适应的给出最佳的聚类数，而且可以验证聚类的有效性，达到最佳聚类的目的。-In the original fcm algorithm based on the number of clusters on the algorithm and the weighted index m given c preferred method, and then out of the fcm parameter optimization adaptive algorithm, artificial data and data validation with real background can be seen that algorithm is effective, the adaptive algorithm can give the best clustering number, and can verify the validity of clustering, to achieve the best clustering purposes. Platform: |
Size: 267264 |
Author:严德春 |
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Description: 基于WEKA平台的文本聚类研究与实现
文本聚类是文本挖掘领域的一个重要研究分支，是聚类方法在文本处理领域的应用。本文对基于空间向量模型的文本聚类过程做了较深入的讨论和总结，利用文本语料库，基于数据挖掘工具研究并实现了文本聚类的过程。本文首先给出了文本聚类的思想和过程，回顾了文本聚类领域的已有成果，列举了文本聚类领域在特征表示、特征提取等方面的基础研究工作。另外，本文回顾了现有的文本聚类算法，以及常用的文本聚类效果评价指标。在研究了已有成果的基础上，本文利用20 Newsgroup文本语料库，针对向量空间表示模型，在开源的数据挖掘平台WEKA上实现了文本预处理和k-means聚类算法，并根据实际聚类效果，就文本表示、特征选择、特征降维、等方面提出优化方案。-Text clustering is an important field of text mining research branch, is the clustering in the field of text processing applications. In this paper, based on vector space model for text clustering process to do a more in-depth discussion and summary, the use of the text corpus, based on data mining tools to study and realize the document clustering process. This paper shows the ideas and text clustering process, reviewed the existing text clustering results of the field, citing the field of document clustering in the feature representation, feature extraction and other aspects of basic research. In addition, the paper reviews the existing text clustering algorithm, as well as common text clustering validity. In the study has been based on the results, we use 20 Newsgroup corpus, for the vector space representation model, in the WEKA open source data mining platform to achieve a text preprocessing and k-means clustering algorithm, and according to the actual clustering effect to the tex Platform: |
Size: 1022976 |
Author:yueyue |
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Description: 在原始的 fcm 算法基础上，对算法中的聚类数 c 和加权指数 m 给出优选方法，
进而而出了 fcm 参数优选自适应算法，通过人造数据与具有实际背景的数据验证可以看出
该算法是有效的，该算法不但可以自适应的给出最佳的聚类数，而且可以验证聚类的有效性，
达到最佳聚类的目的-Fcm algorithm in the original, based on the number of clustering algorithms and the weighted index m given c preferred method, and then out of the fcm parameter optimization adaptive algorithm, synthetic data and practical background in data validation can be seen that the algorithm is effective, the adaptive algorithm can not only give the best number of clusters, and clustering can verify the validity of the purpose to achieve the best clustering Platform: |
Size: 3072 |
Author:谢阳 |
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Description: A fault identification with fuzzy C-Mean clustering
algorithm based on improved ant colony algorithm (ACA) is
presented to avoid local optimization in iterative process of
fuzzy C-Mean (FCM) clustering algorithm and the difficulty in
fault classification. In the algorithm, the problem of fault
identification is translated to a constrained optimized
clustering problem. Using heuristic search of colony can find
good solutions. And according to the information content of
cluster center, it could merger surrounding data together to
cause clustering identification. The algorithm may identify
fuzzy clustering numbers and initial clustering center. It can
also prevent data classification from causing some errors.
Thus, applying in fault diagnosis shows validity of computing
and credibility of identification results. Platform: |
Size: 273408 |
Author:rishi |
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Description: This paper presents a new cluster validity index for nding a suitable number of
fuzzy clusters with crisp and fuzzy data. The new index, called the ECAS-index, contains
exponential compactness and separation measures. These measures indicate homogeneity
within clusters and heterogeneity between clusters, respectively. Moreover, a fuzzy c-mean
algorithm is used for fuzzy clustering with crisp data, and a fuzzy k-numbers clustering is
used for clustering with fuzzy data. In comparison to other indices, it is evident that the proposed index is more e ffective and robust under
di fferent conditions of data sets, such as noisy environments and large data sets. Platform: |
Size: 3467264 |
Author:m |
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Description: 一种层次聚类的方法，用matlab实现的源代码，可对点数据等进行聚类分析-A hierarchical clustering method, using matlab to achieve the source code, can be carried to the point of data clustering analysis Platform: |
Size: 1024 |
Author:123 |
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Description: 可以自适应的给出最佳的聚类数， 而且可以验证聚类的有效性，
达到最佳聚类的目的-You can give the best adaptive number of clusters, and can verify the validity of clustering, to achieve the purpose of optimal clustering Platform: |
Size: 1024 |
Author:matlab |
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Description: 与模糊数学相关的,模糊聚类分析的有效性问题初探,FCM算法是论文中不可或缺的部分,在此将FCM算法的两种迭代形式的MATLAB代码写于下,也许有的同学会用得着:-Fuzzy clustering analysis and fuzzy validity problems of the mathematically related, FCM algorithm is an integral part of the paper, in the form of the FCM two iterative algorithm MATLAB code written in the next, and perhaps some students will use have a: Platform: |
Size: 2048 |
Author:陈红 |
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Description: 欢迎使用和评述此工具箱，您的意见是对我们工作的支持。
此工具适合于不同有效性指标的性能比较，改进代码用于不同的应用问题等等。
(1) NCT的内容
NCT包括4个外部有效性指标和8种内部有效性指标，编制的程序文件"validity_Index.m"用于调用它们
(2) 主文件 "mainClusterValidationNC.m" 的内容
主文件设计为如何使用PAM聚类算法、如何使用有效性指标和方法来估计聚类个数。(Help file of Toolbox for estimating the number of clusters (NCT)
(Version 2.0)
Your comments are welcome at:
http://www.mathworks.com/matlabcentral/fileexchange/13916
E-mail: wangkjun@yahoo.com
(1) Contents of NCT
The NCT includes 4 External validity indices and 8 internal validity indices, and the sub-routine "validity_Index.m" is designed to use them.
This tool is suitable for the research work such as the performance comparison of different indices on estimation of the number of clusters, algorithm design by improving part codes of this toolbox.
(2) Contents of main file "mainClusterValidationNC.m"
It is designed to use validity indices to estimate the number of clusters (NC) for PAM and K-means clustering algorithms.) Platform: |
Size: 37888 |
Author:CCGC
|
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Search - clustering validity