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文件名称:FuzzyClusteringToolbox

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    2017-08-12
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    1.98mb
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模糊聚类分析一般是指根据研究对象本身的属性来构造模糊矩阵,并在此基础上根据一定的隶属度来确定聚类关系,即用模糊数学的方法把样本之间的模糊关系定量的确定,从而客观且准确地进行聚类。聚类就是将数据集分成多个类或簇,使得各个类之间的数据差别应尽可能大,类内之间的数据差别应尽可能小,即为“最小化类间相似性,最大化类内相似性”原则。(Fuzzy clustering analysis generally refers to the construction of fuzzy matrix based on the attributes of the research object itself, and on this basis, according to a certain degree of membership to determine the cluster relationship, that is, fuzzy mathematics method to determine the fuzzy relationship between the quantitative , So that the objective and accurate clustering. Clustering is the data set is divided into multiple classes or clusters, so that the data between the various categories should be as large as possible, the data between the class should be as small as possible, that is, "minimize the similarity between classes, Class similarity "principle.)
相关搜索: 模糊聚类算法

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下载文件列表

FuzzyClusteringToolbox\Demos\clusteringexamples\motorcycle\FCMcall.m
FuzzyClusteringToolbox\Demos\clusteringexamples\motorcycle\GGcall.m
FuzzyClusteringToolbox\Demos\clusteringexamples\motorcycle\GKcall.m
FuzzyClusteringToolbox\Demos\clusteringexamples\motorcycle\Kmeanscall.m
FuzzyClusteringToolbox\Demos\clusteringexamples\motorcycle\Kmedoidcall.m
FuzzyClusteringToolbox\Demos\clusteringexamples\motorcycle\MotorCycle.txt
FuzzyClusteringToolbox\Demos\clusteringexamples\synthetic\FCMcall.m
FuzzyClusteringToolbox\Demos\clusteringexamples\synthetic\GGcall.m
FuzzyClusteringToolbox\Demos\clusteringexamples\synthetic\GKcall.m
FuzzyClusteringToolbox\Demos\clusteringexamples\synthetic\Kmeanscall.m
FuzzyClusteringToolbox\Demos\clusteringexamples\synthetic\Kmedoidcall.m
FuzzyClusteringToolbox\Demos\clusteringexamples\synthetic\nDexample.m
FuzzyClusteringToolbox\Demos\clustevalexample\data2.txt
FuzzyClusteringToolbox\Demos\clustevalexample\evalexample.m
FuzzyClusteringToolbox\Demos\comparing\FCMcall.m
FuzzyClusteringToolbox\Demos\comparing\GGcall.m
FuzzyClusteringToolbox\Demos\comparing\GKcall.m
FuzzyClusteringToolbox\Demos\comparing\Kmeanscall.m
FuzzyClusteringToolbox\Demos\comparing\Kmedoidcall.m
FuzzyClusteringToolbox\Demos\comparing\modvalidity.m
FuzzyClusteringToolbox\Demos\comparing\nDexample.m
FuzzyClusteringToolbox\Demos\normexample\data3.txt
FuzzyClusteringToolbox\Demos\normexample\normexample.m
FuzzyClusteringToolbox\Demos\optnumber\modvalidity.m
FuzzyClusteringToolbox\Demos\optnumber\MotorCycle.txt
FuzzyClusteringToolbox\Demos\optnumber\optnumber.m
FuzzyClusteringToolbox\Demos\PCAexample\nDexample.m
FuzzyClusteringToolbox\Demos\PCAexample\PCAexample.m
FuzzyClusteringToolbox\Demos\projection\IRIS.MAT
FuzzyClusteringToolbox\Demos\projection\visual_call.m
FuzzyClusteringToolbox\Demos\projection\WINEDAT.TXT
FuzzyClusteringToolbox\Demos\projection\wisconsin.wk1
FuzzyClusteringToolbox\FUZZCLUST\clusteval.m
FuzzyClusteringToolbox\FUZZCLUST\clust_denormalize.m
FuzzyClusteringToolbox\FUZZCLUST\clust_normalize.m
FuzzyClusteringToolbox\FUZZCLUST\FCMclust.m
FuzzyClusteringToolbox\FUZZCLUST\FuzSam.m
FuzzyClusteringToolbox\FUZZCLUST\GGclust.m
FuzzyClusteringToolbox\FUZZCLUST\GKclust.m
FuzzyClusteringToolbox\FUZZCLUST\Kmeans.m
FuzzyClusteringToolbox\FUZZCLUST\Kmedoid.m
FuzzyClusteringToolbox\FUZZCLUST\nDexample.m
FuzzyClusteringToolbox\FUZZCLUST\PCA.m
FuzzyClusteringToolbox\FUZZCLUST\PROJEVAL.M
FuzzyClusteringToolbox\FUZZCLUST\SAMMON.M
FuzzyClusteringToolbox\FUZZCLUST\SAMSTR.M
FuzzyClusteringToolbox\FUZZCLUST\validity.m
FuzzyClusteringToolbox\FuzzyClusteringToolbox.pdf
FuzzyClusteringToolbox\Demos\clusteringexamples\motorcycle
FuzzyClusteringToolbox\Demos\clusteringexamples\synthetic
FuzzyClusteringToolbox\Demos\clusteringexamples
FuzzyClusteringToolbox\Demos\clustevalexample
FuzzyClusteringToolbox\Demos\comparing
FuzzyClusteringToolbox\Demos\normexample
FuzzyClusteringToolbox\Demos\optnumber
FuzzyClusteringToolbox\Demos\PCAexample
FuzzyClusteringToolbox\Demos\projection
FuzzyClusteringToolbox\Demos
FuzzyClusteringToolbox\FUZZCLUST
FuzzyClusteringToolbox

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