sort orientations into clusters
Syntax
[c,center] = doHCluster(ori,'numCluster',n)
[c,center] = doHCluster(ori,'maxAngle',omega)
Input
ori | orientation |
n | number of clusters |
omega | maximum angle |
Output
c | list of clusters |
center | center of the clusters |
Example
% generate orientation clustered around 5 centers
cs = crystalSymmetry('m-3m');
center = orientation.rand(5,cs);
odf = unimodalODF(center,'halfwidth',5*degree)
ori = odf.discreteSample(3000);
odf = SO3FunRBF (m-3m → xyz)
<strong>multimodal components</strong>
kernel: de la Vallee Poussin, halfwidth 5°
center: 5 orientations
Bunge Euler angles in degree
phi1 Phi phi2 weight
159.595 107.686 188.347 0.2
152.461 69.8275 70.1477 0.2
16.3405 42.5481 345.104 0.2
47.8862 89.1992 259.787 0.2
0.600726 93.0248 199.439 0.2
% find the clusters and its centers
tic; [c,centerRec] = calcCluster(ori,'method','hierarchical','numCluster',5); toc
Elapsed time is 4.706812 seconds.
% visualize result
oR = fundamentalRegion(cs)
plot(oR)
oR = orientationRegion
crystal symmetry: 432
max angle: 62.7994°
face normales: 14
vertices: 24
![](images/orientation.doHClustering_01.png)
hold on
plot(ori,ind2color(c))
caxis([1,5])
plot(center,'MarkerSize',10,'MarkerFaceColor','k','MarkerEdgeColor','k')
plot(centerRec,'MarkerSize',10,'MarkerFaceColor','r','MarkerEdgeColor','k')
hold off
plot 2000 random orientations out of 3000 given orientations
![](images/orientation.doHClustering_02.png)
%check the accuracy of the recomputed centers
min(angle_outer(center,centerRec)./degree)
ans =
11.2429 0.2440 0.1694 0.2656