k-means algorithm takes parameters k and then advance the input data object is divided into n-k-clustering in order to make the clustering obtained to meet: the object of the same cluster in the high similarity and objects in different cluster little similarity. Cluster similarity is the use of objects in each cluster obtained by means of a " central object" (center of gravity) to be calculated.

kmeans\Debug\kmeans.exe
......\.....\kmeans.ilk
......\.....\kmeans.pdb
......\kmeans\Debug\BuildLog.htm
......\......\.....\FileClass.obj
......\......\.....\kmeans.exe.intermediate.manifest
......\......\.....\main.obj
......\......\.....\mt.dep
......\......\.....\vc90.idb
......\......\.....\vc90.pdb
......\......\FileClass.cpp
......\......\FileClass.h
......\......\kmeans.vcproj
......\......\kmeans.vcproj.ZhangMuhua-PC.Zhang Muhua.user
......\......\main.cpp
......\kmeans.ncb
......\kmeans.sln
......\kmeans.suo
......\......\Debug
......\Debug
......\kmeans
kmeans

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