hierarchical clustering of rotations and vectors
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
166.607 145.688 2.15807 0.2
29.3743 98.7971 318.122 0.2
244.614 81.8433 359.86 0.2
125.618 100.236 299.884 0.2
277.327 130.467 54.8851 0.2
% find the clusters and its centers
tic; [c,centerRec] = calcCluster(ori,'method','hierarchical','numCluster',5); toc
Elapsed time is 3.408734 seconds.
% visualize result
oR = fundamentalRegion(cs)
plot(oR)
oR = orientationRegion
crystal symmetry: 432
max angle: 62.7994°
face normales: 14
vertices: 24
![](images/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/doHClustering_02.png)
%check the accuracy of the recomputed centers
min(angle_outer(center,centerRec)./degree)
ans =
0.3163 0.2621 0.1132 0.1871 0.1677