doHClustering edit page

hirarchical 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.calcOrientations(3000);
odf = ODF (m-3m → xyz)
 
  Radially symmetric portion:
    kernel: de la Vallee Poussin, halfwidth 5°
    center: Rotations: 5 x 1
    weight: 1
% find the clusters and its centers
tic; [c,centerRec] = calcCluster(ori,'method','hierarchical','numCluster',5); toc
Elapsed time is 3.757474 seconds.
% visualize result
oR = fundamentalRegion(cs)
plot(oR)
oR = orientationRegion
 
 crystal symmetry:  432
 max angle: 62.7994°
 face normales: 14
 vertices: 24
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
%check the accuracy of the recomputed centers
min(angle_outer(center,centerRec)./degree)
ans =
    0.1650    8.6538    0.4948   11.3122    0.2257