文件名称:EvaluateMetric
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Clustering Evaluation: Evaluate the clustering result by accuracy and normalized mutual information
Deng Cai, Xiaofei He, and Jiawei Han, "Document Clustering Using Locality Preserving Indexing", in IEEE TKDE, 2005. Bibtex source
bestMap
hungarian
MutualInfo
===========================================
fea = rand(50,70)
gnd = [ones(10,1) ones(15,1)*2 ones(10,1)*3 ones(15,1)*4]
res = kmeans(fea,4)
res = bestMap(gnd,res)
============= evaluate AC: accuracy ==============
AC = length(find(gnd == res))/length(gnd)
============= evaluate MIhat: nomalized mutual information =================
MIhat = MutualInfo(gnd,res)
-Clustering Evaluation: Evaluate the clustering result by accuracy and normalized mutual information
Deng Cai, Xiaofei He, and Jiawei Han, "Document Clustering Using Locality Preserving Indexing", in IEEE TKDE, 2005. Bibtex source
bestMap
hungarian
MutualInfo
===========================================
fea = rand(50,70)
gnd = [ones(10,1) ones(15,1)*2 ones(10,1)*3 ones(15,1)*4]
res = kmeans(fea,4)
res = bestMap(gnd,res)
============= evaluate AC: accuracy ==============
AC = length(find(gnd == res))/length(gnd)
============= evaluate MIhat: nomalized mutual information =================
MIhat = MutualInfo(gnd,res)
===========================================
Deng Cai, Xiaofei He, and Jiawei Han, "Document Clustering Using Locality Preserving Indexing", in IEEE TKDE, 2005. Bibtex source
bestMap
hungarian
MutualInfo
===========================================
fea = rand(50,70)
gnd = [ones(10,1) ones(15,1)*2 ones(10,1)*3 ones(15,1)*4]
res = kmeans(fea,4)
res = bestMap(gnd,res)
============= evaluate AC: accuracy ==============
AC = length(find(gnd == res))/length(gnd)
============= evaluate MIhat: nomalized mutual information =================
MIhat = MutualInfo(gnd,res)
-Clustering Evaluation: Evaluate the clustering result by accuracy and normalized mutual information
Deng Cai, Xiaofei He, and Jiawei Han, "Document Clustering Using Locality Preserving Indexing", in IEEE TKDE, 2005. Bibtex source
bestMap
hungarian
MutualInfo
===========================================
fea = rand(50,70)
gnd = [ones(10,1) ones(15,1)*2 ones(10,1)*3 ones(15,1)*4]
res = kmeans(fea,4)
res = bestMap(gnd,res)
============= evaluate AC: accuracy ==============
AC = length(find(gnd == res))/length(gnd)
============= evaluate MIhat: nomalized mutual information =================
MIhat = MutualInfo(gnd,res)
===========================================
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下载文件列表
bestMap.m
hungarian.m
MutualInfo.m
hungarian.m
MutualInfo.m