K-means clustering algorithm to achieve a two-dimensional clustering extends to any dimension of the cluster sample points, the code attached to the principle of detailed instructions, and tips relevant examples, good results

K均值聚类N维\0.jpg
............\100.txt
............\KAVE.APS
............\KAVE.clw
............\KAVE.cpp
............\KAVE.dsp
............\KAVE.dsw
............\KAVE.h
............\KAVE.ncb
............\KAVE.opt
............\KAVE.plg
............\KAVE.rc
............\KAVEDlg.cpp
............\KAVEDlg.h
............\kaverage.cpp
............\kaverage.h
............\ReadMe.txt
............\Resource.h
............\result.jpg
............\StdAfx.cpp
............\StdAfx.h
............\res\KAVE.ico
............\...\KAVE.rc2
............\Debug\KAVE.bsc
............\.....\KAVE.exe
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............\.....\KAVE.sbr
............\.....\KAVEDlg.obj
............\.....\KAVEDlg.sbr
............\.....\kaverage.obj
............\.....\kaverage.sbr
............\.....\StdAfx.obj
............\.....\StdAfx.sbr
............\.....\vc60.idb
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............\res
............\Debug
K均值聚类N维

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