I have a dataset of over 67 000 records, and I'm trying to run a k-means cluster analysis on that. Orange returns a memory error. The data is in an excel file, but I also tried to load it from a csv. Omitting some records and downsizing it under 65k records also couldnt help. Hierarchical clustering also doesnt work for the same reason.
Does anyone have any ideas on what to do here?
Error code:
MemoryError
Traceback (most recent call last):
File "C:\Python34\lib\site-packages\Orange\widgets\gui.py", line 2220, in b.button = btn = button(b, master, label, callback=lambda: do_commit()) File "C:\Python34\lib\site-packages\Orange\widgets\gui.py", line 2191, in do_commit commit()
File "C:\Python34\lib\site-packages\Orange\widgets\unsupervised\owkmeans.py", line 246, in run self.run_optimization()
File "C:\Python34\lib\site-packages\Orange\widgets\unsupervised\owkmeans.py", line 225, in run_optimization self.optimization_runs.append((k, kmeans(self.data)))
File "C:\Python34\lib\site-packages\Orange\projection\base.py", line 28, in call clf = self.fit(data.X, data.Y)
File "C:\Python34\lib\site-packages\Orange\clustering\kmeans.py", line 23, in fit proj = proj.fit(X, Y)
File "C:\Python34\lib\site-packages\sklearn\cluster\k_means_.py", line 821, in fit n_jobs=self.n_jobs)
File "C:\Python34\lib\site-packages\sklearn\cluster\k_means_.py", line 277, in k_means tol = _tolerance(X, tol)
File "C:\Python34\lib\site-packages\sklearn\cluster\k_means_.py", line 162, in _tolerance variances = np.var(X, axis=0)
File "C:\Python34\lib\site-packages\numpy\core\fromnumeric.py", line 2938, in var keepdims=keepdims)
File "C:\Python34\lib\site-packages\numpy\core_methods.py", line 102, in _var x = asanyarray(arr - arrmean) MemoryError