Scatter-plot of data in which the cluster membership is coded by colors.

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Mark S
Mark S 2021-5-31
编辑： Mark S 2021-6-1
Hello, I have created a dendrogram of my given data.
NumCluster = 1566;
dist = pdist(alldata, 'euclidean');
GroupsMatrix = linkage(dist, 'complete');
clust = cluster(GroupsMatrix, 'maxclust', NumCluster);
E = evalclusters(alldata,clust,'CalinskiHarabasz')
[H,T,perm] = dendrogram(GroupsMatrix, 1566, 'colorthreshold', 'default');
I want to create now a scatter-plot of the data in which the cluster membership is coded by colors. I have tried to implement it like this
gscatter (alldata(:,1),alldata(:,2), E.OptimalY,'rbgk','xod')
Update:
The error is now gone but all the scatter plot has the same color. How can I cluster the membership by different colors? And is my E chosen correctly for the number of clusters? For my E I have 1566 cluster. I did not know if this is okay.
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the cyclist
the cyclist 2021-5-31
It can be difficult to diagnose issues without the data. Specifically, we don't see how E is defined. Can you upload the data, such that we can actually run your code and reproduce the error?

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回答（1 个）

KSSV
KSSV 2021-6-1
编辑：KSSV 2021-6-1
gscatter (alldata(:,1),alldata(:,2),clust,'rbgk','xod')
Check the option E.OptimalY, it is empty []. So all the points are shown by same color/ maekers.
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KSSV
KSSV 2021-6-1
编辑：KSSV 2021-6-1
alldata = csvread('Data.csv') ;
NumCluster = 10; % <-----change cluster number here
dist = pdist(alldata, 'euclidean');
GroupsMatrix = linkage(dist, 'complete');
clust = cluster(GroupsMatrix, 'maxclust', NumCluster);
E = evalclusters(alldata,clust,'CalinskiHarabasz') ;
gscatter (alldata(:,1),alldata(:,2),clust)
Mark S
Mark S 2021-6-1
编辑：Mark S 2021-6-1
Thanks. It works fine. One additional question: How can I find an optimal cluster number? Is it best to vary the NumCluster myself or is there another method? I have found this in the matlab help:
https://it.mathworks.com/matlabcentral/answers/76879-determining-the-optimal-number-of-clusters-in-kmeans-technique
klist=2:500;%the number of clusters you want to try
myfunc = @(X,K)(kmeans(X, K));
eva = evalclusters(alldata,myfunc,'CalinskiHarabasz','klist',klist)
classes=kmeans(alldata,eva.OptimalK);
I get here for my optimalK=3. But I am not sure if this is ok. Is the calculation for the optimal cluster numbers so ok?

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