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文件名称:kmeans

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    2013-03-16
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    611.8kb
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利用k-means算法进行聚类,K-means算法以欧式距离作为相似度测度,它是求对应某一初始聚类中心向量V最有分类,使得评价指标J最小。算法采用误差平方和准则函数作为聚类准则函数。-Algorithm using k-means clustering, K-means algorithm Euclidean distance as a similarity measure, it is the pursuit of the vector V corresponding to a initial cluster centers classification, making the minimum evaluation J. The algorithm uses the error square and guidelines function as the clustering criterion function.
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下载文件列表

kmeans/Debug/kmeans.exe
kmeans/Debug/kmeans.ilk
kmeans/Debug/kmeans.pdb
kmeans/ipch/kmeans-5252bde7/kmeans-7736984b.ipch
kmeans/kmeans/Debug/cl.command.1.tlog
kmeans/kmeans/Debug/CL.read.1.tlog
kmeans/kmeans/Debug/CL.write.1.tlog
kmeans/kmeans/Debug/kmeans.exe.embed.manifest
kmeans/kmeans/Debug/kmeans.exe.embed.manifest.res
kmeans/kmeans/Debug/kmeans.exe.intermediate.manifest
kmeans/kmeans/Debug/kmeans.lastbuildstate
kmeans/kmeans/Debug/kmeans.log
kmeans/kmeans/Debug/kmeans.obj
kmeans/kmeans/Debug/kmeans_manifest.rc
kmeans/kmeans/Debug/link-cvtres.read.1.tlog
kmeans/kmeans/Debug/link-cvtres.write.1.tlog
kmeans/kmeans/Debug/link.6208-cvtres.read.1.tlog
kmeans/kmeans/Debug/link.6208-cvtres.write.1.tlog
kmeans/kmeans/Debug/link.6208.read.1.tlog
kmeans/kmeans/Debug/link.6208.write.1.tlog
kmeans/kmeans/Debug/link.command.1.tlog
kmeans/kmeans/Debug/link.read.1.tlog
kmeans/kmeans/Debug/link.write.1.tlog
kmeans/kmeans/Debug/mt.command.1.tlog
kmeans/kmeans/Debug/mt.read.1.tlog
kmeans/kmeans/Debug/mt.write.1.tlog
kmeans/kmeans/Debug/rc.command.1.tlog
kmeans/kmeans/Debug/rc.read.1.tlog
kmeans/kmeans/Debug/rc.write.1.tlog
kmeans/kmeans/Debug/vc100.idb
kmeans/kmeans/Debug/vc100.pdb
kmeans/kmeans/kmeans.c
kmeans/kmeans/kmeans.vcxproj
kmeans/kmeans/kmeans.vcxproj.filters
kmeans/kmeans/kmeans.vcxproj.user
kmeans/kmeans.sdf
kmeans/kmeans.sln
kmeans/kmeans.suo
kmeans/ipch/kmeans-5252bde7
kmeans/kmeans/Debug
kmeans/Debug
kmeans/ipch
kmeans/kmeans
kmeans

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