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Separating 1D data into clusters and counting the amount of elements in each

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I have a 1-dimensional array with index values of extremes I found in a much larger dataset, ordered from lowest to highest. I wish to seek out if there is any data clustering within this array. What's the simplest way to do this? The total amount of clusters is unknown beforehand. A cluster can be defined as any values that are within a certain predefined distance from each other. I'd like to know the amount of clusters found, and an array with the number of values found within each cluster.
I made a simple illustration of what I'd like in the figure below. The crosses indicate values in the array on the number line. Values close together (removed less than a given distance), are grouped together and counted.
So, in this case, the output would be;
5 (number of clusters found)
[3, 2, 1, 1, 5] (size of each cluster)
Thanks in advance!

Accepted Answer

Kelly Kearney
Kelly Kearney on 21 Aug 2021
If the cluster tolerance defines how close points need to be to their nearest neighbor in order to be called a cluster, then you just need to check for where the gaps are larger than that tolerance:
% The data
tol = 0.1;
x = [1 1 1 2 2 3 4 5 5 5 5 5];
x = x + rand(size(x))*tol-tol/2;
% Find clusters
dx = diff(x);
isnew = dx > tol;
idx = cumsum([1 isnew]);
ncluster = max(idx)
ncluster = 5
nper = splitapply(@length, idx, idx)
nper = 1×5
3 2 1 1 5
If instead your tolerance sets the maximum distance a point can be from all points in its cluster, then things get a bit trickier.
Menno van Dam
Menno van Dam on 22 Aug 2021
@Image Analyst Thank you for the notifying me about that. Luckily, my data already happened to be sorted, so this wasn't an issue for me. Still, it is useful to keep that in mind.

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More Answers (2)

Image Analyst
Image Analyst on 21 Aug 2021
Edited: Image Analyst on 21 Aug 2021
If you have the Statistics and Machine Learning Toolbox, there is a function that does this. It's called dbscan() after the clustering algorithm of the same name (which should probably be more famous than it is.)
See attached dbscan demo, and Wikipedia description:
Basically it finds all points within a specified distance from other points by daisy chaining its way along. All those points that can be connected by a daisy chain length less than what you specified belong to one cluster. Then it moves on to other unclassified points to find more clusters. Points that are farther away from any point than your distance are not clusters, or in other words they're a cluster of only 1 point.
Here's a very well commented demo that is more tailored to your data:
% First define the data.
x = [1 1 1 2 2 3 4 5 5 5 5 5];
y = zeros(1, length(x));
% Plot it.
plot(x, y, 'rx', 'MarkerSize', 20, 'LineWidth', 3);
ax = gca;
ax.XAxisLocation = 'origin';
grid on;
% Now find clusters.
minDistance = 0.5; % Need to be closer than this to be in the same cluster.
minPointsPerCluster = 2; % A cluster must have at least 2 points to be a valid cluster.
indexes = dbscan([x',y'], minDistance, minPointsPerCluster)
% The value of indexes say what cluster number that point belongs to.
% If the value is -1, it does not belong to a cluster and is a single, isolated point.
% Find the number (count) in each cluster
pointCounts = hist(indexes, -1 : max(indexes))
% It says there are 2 points not in a cluster (single isolated points),and
% then the counts are 3, 2, and 5 for the 3 clusters it found.

Image Analyst
Image Analyst on 22 Aug 2021
You can do this in a single line of code with a call to findgroups() and histcounts():
% First define the data.
x = [1 1 1 2 2 3 4 5 5 5 5 5];
% Now find groups.
g1 = findgroups(x)
% Count the number in each group.
counts1 = histcounts(g1)
% Now with another group of 1's on the end, separated from the first group of 1's.
x = [1 1 1 2 2 3 4 5 5 5 5 5 1 1];
counts2 = histcounts(findgroups(x)) % Combining 2 statements into 1.
You see:
counts1 =
3 2 1 1 5
counts2 =
5 2 1 1 5
as you should.
Image Analyst
Image Analyst on 22 Aug 2021
findgroups() doesn't appear to have a tolerance and appears to expect discrete values, so 22000 and 22001 would be in two different groups. Your original array seemed like it was integers with some repeated so that's why I suggested findgroups(). But if they are slightly different, and you have some tolerance, I believe most experts would recommend you use dbscan(), that is, if you have the Statistics and Machine Learning Toolbox.

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