Are you asking how to divide this into two separate curves, the upper half, and the lower half? I can only assume that is your goal.
There could be many ways to solve this problem. For example, I might use knnsearch, to find the set of points that are nearest to each point, perhaps the 3 nearest neighbors. Then use a graph theoretic scheme to cluster the two croups. But perhaps the easiest way is to build a delaunay triangulation of the entire set. For example:
T = delaunayn(dataB1);
% now, list the set of all edges in that triangulation.
edges = [T(:,[1 2]);T(:,[1 3]);T(:,[2 3])];
% sort the edges to remove the edges that were listed twice.
edges = sort(edges,2);
edges = unique(edges,'rows');
% compute the length of each edge.
edgeLen = sqrt(sum((dataB1(edges(:,1),:) - dataB1(edges(:,2),:)).^2,2));
% discard any edges with a length of more than 5.
k = edgeLen > 5;
edges(k,:) = [];
% see how we did so far, by plotting the edges we have identified
plot([dataB1(edges(:,1),1)';dataB1(edges(:,2),1)'],[dataB1(edges(:,1),2)';dataB1(edges(:,2),2)'],'-o')
That looks pretty good. I've managed to segregate the two curves into two cohesive groups by that scheme, at least, I have done so visually. And, yes, I know there were many other ways I could have done this much. Bit now can we break the curves into two segments? Again, the best way seem graph theoretic in nature. Take a look.
G = graph(edges(:,1),edges(:,2));
plot(G)
Now we can clearly see two disjoint segments. Split them easily now, as...
segmentId = conncomp(G);
C1 = find(segmentId == 1);
C2 = find(segmentId == 2);
plot(dataB1(C1,1),dataB1(C1,2),'ro',dataB1(C2,1),dataB1(C2,2),'b+')
So not that difficult. The only thing I had to do was to choose a distance threshold for the edges. With some more thought, I could probably have done that automatically too.
