Hi!
...I have found a good enough solution, although it only works if the
number of patterns (clusters) is not very big:
def classify(f):
THERESHOLD=0.1
patterns={}
for l in enumerate(f):
found = False
for p,c in patterns.items():
if dist(l,p) < THERESHOLD:
found=True
patterns[p] = c +1
if not found:
patterns[l] = 1
return patterns
This algorithm is O(n*np*m^2). Where n is the number of logs, np the
number of patterns, and m is the log length (i.e. m^2 is the distance
cost). So it's way better O(n^2*m^2) and I can run it for some hours
to get back the results.
I wonder if there is a single threaded/process clustering algorithm
than runs in O(n)?
Cheers,
marc
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