Command "cluster" with big data: it used to work fast but now it works slow

Hello!

I have matrix in variable " dat".

Number of rows = 564372

Number of columns = 11

Each row represents an observation and I need to cluster this data. Command " kmeans" works fast and now I'm trying agglomerative clusterisation. I computed linkage (it took about 8 hours) with the command:

Z=linkage(dat,'centroid','euclidean','savememory','on');

Then I came home and I computed few cluster with different thresholds:

T=cluster(Z,'cutoff',1.4);

I was extremely surprised when I saw that the cluster computation took only 10-15 seconds and the result was fine. Then I saved my linkage data:

Z=dlmwrite('Z-linkage.txt',Z);

Next day I launched Matlab, imported Z-linkage.txt and tryed to compute cluster again. But for this time it works very slow. It may take hours and I don't have any idea what is the problem?

Please help!

Thank you for any suggestion

Answer 1

[John D'Errico]

Since we have absolutely nothing to go on about the actual data, I can only guess.

Clustering tools usually use random starts. That means you may get lucky some times, seeing rapid convergence.

[Kerim Khemraev]

Thank you for reply,

May be, but I computed clusters many times (about 50 times) during one session in Matlab. And it was fast. I don't think that I got many times successful random start.

I can share my data. Here it is:

Z-linkage_file