MATLAB: K-means with predefined centroid

centroldclusterclusteringkmeansMATLAB

Hi,

I need to cluster the data that I have but the problem is I want to cluster the data based on the centroid that I already know. It shouldnt change in the process, I want to keep the centroid as it is.

Any help will be greatly appreciated.

Thanks.

Best Answer

This appears to be much the same question as was asked in cssm several days ago; several clarifying questions were given there and some mechanisms to proceed were suggested. cssm post

Related Solutions

MATLAB: Save different values of the same table from a loop

T2 = cell(length(centroid_clust),1)
for k=1:length(centroid_clust)
     
  separate_areas(1,k)=[area_clust(k).Area];
   c=[centroid_clust(k).Centroid];
  text(c(1), c(2), sprintf('%d', k));  
Cluster(k,:)=k;
Area_of_cluster(k,:)=area_clust(k).Area;
Eccentricity_of_cluster(k,:)=eccentricity_clust(k).Eccentricity ;  
Euler_of_cluster(k,:)=euler_clust(k).EulerNumber;
Perimeter_of_cluster(k,:)=perimeter_clust(k).Perimeter;
Orientation_of_cluster(k,:)=orientation_clust(k).Orientation;
T2{k}= table(Cluster,Area_of_cluster,Eccentricity_of_cluster,Euler_of_cluster,Perimeter_of_cluster,Orientation_of_cluster)
end

MATLAB: Assigning a new data point to a cluster in hierarchical clustering

If you know your maxclust is 2, then just use 2-means. Hierarchical clustering and linkage will give you an insight about the number of natural clusters in your data. When you found that k' (e.g. using 'cutoff') based on hierarchical clustering, you can use k'-mean.

Best Answer

Related Solutions

MATLAB: Save different values of the same table from a loop

MATLAB: Assigning a new data point to a cluster in hierarchical clustering

Related Question