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2Â¥: Originally posted by libralibra at 2012-07-06 19:13:54
kmeans²»ÓÃϰ¡,matlabÄÚÖõÄÓÐ

kmeans
K-means clustering

Syntax

IDX = kmeans(X,k)
= kmeans(X,k)
= kmeans(X,k)
= kmeans(X,k)
= kmeans(...,param1,val1,param2,val2,...)

Description ...

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3Â¥2012-10-22 08:27:54
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libralibra

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csgt0: ½ð±Ò+1, ¶àлӦÖú 2012-10-22 15:56:51
kmeans²»ÓÃϰ¡,matlabÄÚÖõÄÓÐ
CODE:
kmeans
K-means clustering

Syntax

IDX = kmeans(X,k)
[IDX,C] = kmeans(X,k)
[IDX,C,sumd] = kmeans(X,k)
[IDX,C,sumd,D] = kmeans(X,k)
[...] = kmeans(...,param1,val1,param2,val2,...)

Description

IDX = kmeans(X,k) partitions the points in the n-by-p data matrix X into k clusters. This iterative partitioning minimizes the sum, over all clusters, of the within-cluster sums of point-to-cluster-centroid distances. Rows of X correspond to points, columns correspond to variables. kmeans returns an n-by-1 vector IDX containing the cluster indices of each point. By default, kmeans uses squared Euclidean distances.

ÊäÈën*p¾ØÕóx,Òª¾ÛÀàµÄ¸öÊýk,·µ»Øn*1µÄÏòÁ¿,±íʾÿÐеÄÄǸöµãÊôÓÚÄĸö¾ÛÀà
½«ÄãÇó³öµÄÿ·ùͼµÄsiftÌØÕ÷¿´×öÒ»¸öµã(¼ÙÉèÓÐ6¸öÌØÕ÷),´æÎªÒ»ÐÐ,¶àÉÙ¸öͼƬ¾Í¶àÉÙÐÐ(¼ÙÉèΪn),×îºó¹¹ÔìÒ»¸ö¾ØÕón*6µ±×öx,¾ÛÀà¸öÊýk,½«xºÍk×÷Ϊ²ÎÊýÖ±½Óµ÷ÓÃind = kmeans(x,k)¾ÍÐÐÁË
×îºóµÄ½á¹ûindÊôÓÚ[1,k],±íʾµ±Ç°µã(¾ÍÊÇÄǸösiftÌØÕ÷¶ÔÓ¦µÄͼÏñ)ÊôÓÚÄĸö¾ÛÀà
matlab/VB/python/c++/Javaд³ÌÐòÇë·¢QQÓʼþ:790404545@qq.com
2Â¥2012-07-06 19:13:54
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