文件名称:KMEANS10
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- 上传时间：2012-11-16
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The k-means algorithm is an algorithm to cluster n objects based on attributes into k partitions, k < n. It is similar to the expectation-maximization algorithm for mixtures of Gaussians in that they both attempt to find the centers of natural clusters in the data. It assumes that the object attributes form a vector space. The objective it tries to achieve is to minimize total intra-cluster variance, or, the squared error function
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下载文件列表
KMEANS10/@Readme-CodeS-SourceS-FR.txt
KMEANS10/@Source telecharge depuis ce lien.url
KMEANS10/code/allocationDeallocation.cpp
KMEANS10/code/bouclePrincipale.cpp
KMEANS10/code/buttonEvent.cpp
KMEANS10/code/constructeurFenetre.cpp
KMEANS10/code/creationImage.cpp
KMEANS10/code/distance.cpp
KMEANS10/code/etirerHistogram.cpp
KMEANS10/code/initialisation.cpp
KMEANS10/code/k-moyennes.cpp
KMEANS10/code/MaFenetre.h
KMEANS10/code/main.cpp
KMEANS10/code/main.h
KMEANS10/code/matriceAppartenance.cpp
KMEANS10/code/minimum.cpp
KMEANS10/code/nouvCentroid.cpp
KMEANS10/code/resouces.rc
KMEANS10/code/walid.cbp
KMEANS10/code/walid.depend
KMEANS10/code/walid.layout
KMEANS10/code
KMEANS10
KMEANS10/@Source telecharge depuis ce lien.url
KMEANS10/code/allocationDeallocation.cpp
KMEANS10/code/bouclePrincipale.cpp
KMEANS10/code/buttonEvent.cpp
KMEANS10/code/constructeurFenetre.cpp
KMEANS10/code/creationImage.cpp
KMEANS10/code/distance.cpp
KMEANS10/code/etirerHistogram.cpp
KMEANS10/code/initialisation.cpp
KMEANS10/code/k-moyennes.cpp
KMEANS10/code/MaFenetre.h
KMEANS10/code/main.cpp
KMEANS10/code/main.h
KMEANS10/code/matriceAppartenance.cpp
KMEANS10/code/minimum.cpp
KMEANS10/code/nouvCentroid.cpp
KMEANS10/code/resouces.rc
KMEANS10/code/walid.cbp
KMEANS10/code/walid.depend
KMEANS10/code/walid.layout
KMEANS10/code
KMEANS10
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