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

KMEANS10
........\@Readme-CodeS-SourceS-FR.txt
........\code
........\....\allocationDeallocation.cpp
........\....\bouclePrincipale.cpp
........\....\buttonEvent.cpp
........\....\constructeurFenetre.cpp
........\....\creationImage.cpp
........\....\distance.cpp
........\....\etirerHistogram.cpp
........\....\initialisation.cpp
........\....\k-moyennes.cpp
........\....\MaFenetre.h
........\....\main.cpp
........\....\main.h
........\....\matriceAppartenance.cpp
........\....\minimum.cpp
........\....\nouvCentroid.cpp
........\....\resouces.rc
........\....\walid.cbp
........\....\walid.depend
........\....\walid.layout

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