文件名称:Cluster_K-means
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- 上传时间:2015-12-08
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文件大小:2.76mb
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已下载:1次
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k中心算法的基本过程是:首先为每个簇随意选择一个代表对象,剩余的对象根据其与每个代表对象的距离(此处距离不一定是欧氏距离,也可能是曼哈顿距离)分配给最近的代表对象所代表的簇;然后反复用非代表对象来代替代表对象,以优化聚类质量。聚类质量用一个代价函数来表示。当一个中心点被某个非中心点替代时,除了未被替换的中心点外,其余各点被重新分配。-The basic process k center algorithm is: First free to choose a delegate object for each cluster, the rest of the object based on its distance each of which represents the object (here the distance is not necessarily a Euclidean distance, Manhattan distance may be) allocated to Recently representatives object represents a cluster and then repeatedly with representatives of non-objects instead represents the object, in order to optimize the quality of clustering. Clustering quality is represented by a cost function. When a center is a non-center alternative, in addition to not replace the center, the rest of the points to be reallocated.
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下载文件列表
Cluster_K-means/
Cluster_K-means/Cluster_K-means.sdf
Cluster_K-means/Cluster_K-means.sln
Cluster_K-means/Cluster_K-means.v12.suo
Cluster_K-means/Cluster_K-means.vcxproj
Cluster_K-means/Cluster_K-means.vcxproj.filters
Cluster_K-means/Debug/
Cluster_K-means/Debug/Cluster_K-means.exe
Cluster_K-means/Debug/Cluster_K-means.ilk
Cluster_K-means/Debug/Cluster_K-means.log
Cluster_K-means/Debug/Cluster_K-means.pdb
Cluster_K-means/Debug/Cluster_K-means.tlog/
Cluster_K-means/Debug/Cluster_K-means.tlog/cl.command.1.tlog
Cluster_K-means/Debug/Cluster_K-means.tlog/CL.read.1.tlog
Cluster_K-means/Debug/Cluster_K-means.tlog/CL.write.1.tlog
Cluster_K-means/Debug/Cluster_K-means.tlog/Cluster_K-means.lastbuildstate
Cluster_K-means/Debug/Cluster_K-means.tlog/link.command.1.tlog
Cluster_K-means/Debug/Cluster_K-means.tlog/link.read.1.tlog
Cluster_K-means/Debug/Cluster_K-means.tlog/link.write.1.tlog
Cluster_K-means/Debug/main.obj
Cluster_K-means/Debug/vc120.idb
Cluster_K-means/Debug/vc120.pdb
Cluster_K-means/main.cpp
Cluster_K-means/Cluster_K-means.sdf
Cluster_K-means/Cluster_K-means.sln
Cluster_K-means/Cluster_K-means.v12.suo
Cluster_K-means/Cluster_K-means.vcxproj
Cluster_K-means/Cluster_K-means.vcxproj.filters
Cluster_K-means/Debug/
Cluster_K-means/Debug/Cluster_K-means.exe
Cluster_K-means/Debug/Cluster_K-means.ilk
Cluster_K-means/Debug/Cluster_K-means.log
Cluster_K-means/Debug/Cluster_K-means.pdb
Cluster_K-means/Debug/Cluster_K-means.tlog/
Cluster_K-means/Debug/Cluster_K-means.tlog/cl.command.1.tlog
Cluster_K-means/Debug/Cluster_K-means.tlog/CL.read.1.tlog
Cluster_K-means/Debug/Cluster_K-means.tlog/CL.write.1.tlog
Cluster_K-means/Debug/Cluster_K-means.tlog/Cluster_K-means.lastbuildstate
Cluster_K-means/Debug/Cluster_K-means.tlog/link.command.1.tlog
Cluster_K-means/Debug/Cluster_K-means.tlog/link.read.1.tlog
Cluster_K-means/Debug/Cluster_K-means.tlog/link.write.1.tlog
Cluster_K-means/Debug/main.obj
Cluster_K-means/Debug/vc120.idb
Cluster_K-means/Debug/vc120.pdb
Cluster_K-means/main.cpp
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