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cmeansClustering.m
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cmeansClustering.m
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function [cmeansTest,cmeansCluster,cmeansAcc,exponentValue] = cmeansClustering(trainingData,testingData,testingClass)
p = 1.1;
u=1;
for p=1.1:0.1:3.5
options = [p 150 0.0000001 0];
exponentValue(u) = p;
[centersCM, ~]= fcm(trainingData', 6,options); %6 clusters
totalDelta = 0;
%i = news index
%j = cluster index
%k = parameters index
for i = 1:length(testingData)
for j = 1:size(centersCM,1)
for k = 1:size(centersCM,2)
%distance between each parameter k and each cluster j of news i
delta = (testingData(k,i)-centersCM(j,k))^2;
totalDelta = totalDelta + delta;
end
%distance of news i to cluster j
dist(j,i) = sqrt(totalDelta);
totalDelta = 0;
end
[~,ClusterIndex] = min(dist(:,i));
cluster(i) = ClusterIndex;
end
fakeCMeans = mode(cluster);
cmeansTest = testingClass(1,:);
cmeansCluster = cluster;
for i = 1:length(cmeansCluster)
if cmeansCluster(i) ~= fakeCMeans
cmeansCluster(i) = 1;
else
cmeansCluster(i) = 0;
end
end
stats = confusionmatStats(cmeansTest,cmeansCluster);
cmeansAcc(u) = stats.accuracy;
u=u+1;
end
%%%%%%%%%%%%%%%%%%%%%%Highest Vale%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
maximum = max(cmeansAcc);
[~,highestExponent]=find(cmeansAcc==maximum);
highestExponent = highestExponent/10 + 1.1;
options = [ highestExponent(1) 150 0.0000001 0];
[centersCM, ~]= fcm(trainingData', 6,options); %6 clusters
totalDelta = 0;
%i = news index
%j = cluster index
%k = parameters index
for i = 1:length(testingData)
for j = 1:size(centersCM,1)
for k = 1:size(centersCM,2)
%distance between each parameter k and each cluster j of news i
delta = (testingData(k,i)-centersCM(j,k))^2;
totalDelta = totalDelta + delta;
end
%distance of news i to cluster j
dist(j,i) = sqrt(totalDelta);
totalDelta = 0;
end
[~,ClusterIndex] = min(dist(:,i));
cluster(i) = ClusterIndex;
end
fakeCMeans = mode(cluster);
cmeansTest = testingClass(1,:);
cmeansCluster = cluster;
for i = 1:length(cmeansCluster)
if cmeansCluster(i) ~= fakeCMeans
cmeansCluster(i) = 1;
else
cmeansCluster(i) = 0;
end
end
end