doHClustering edit page

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 → y↑→x)
 
  multimodal components
  kernel: de la Vallee Poussin, halfwidth 5°
  center: 5 orientations
 
  Bunge Euler angles in degree
     phi1     Phi    phi2  weight
  156.958 109.836 223.608     0.2
  9.33344 126.207 190.491     0.2
  197.878 76.1995 48.4488     0.2
  156.716 113.621 184.888     0.2
  151.332 117.797 66.3984     0.2

find the clusters and its centers

[c,centerRec] = calcCluster(ori,'method','hierarchical','numCluster',5);

visualize result

plot(ori,ind2color(c),'axisAngle')
plot 2000 random orientations out of 3000 given orientations
%check the accuracy of the recomputed centers
min(angle_outer(center,centerRec)./degree)
ans =
    0.3032    0.3298    0.2366    0.5450    0.1580

alternative approach to compute the cluster centers

odfRec = calcDensity(ori)
[~,centerRec2] = max(odfRec,'numLocal',5);
min(angle_outer(center,centerRec2)./degree)
odfRec = SO3FunHarmonic (m-3m → y↑→x)
  bandwidth: 25
  weight: 1
 
ans =
    0.4582    0.6089    0.1909    0.8792    0.6571