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POOptimizing clustering in Python
 

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  1. POOptimizing clustering in Python
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    <p>I wrote my own clustering algorithm (bad, I know) for my problem. It works well, but could work faster.</p> <p>Algorithm takes list of values (1D) as in input, and works like this:</p> <ol> <li>For each cluster, calculate distance to closest neighbor cluster</li> <li>Select the cluster A which has smallest distance to neighbor B</li> <li>If distance between A and B is less then threshold, <strong>return</strong></li> <li>Combine A and B</li> <li>Goto 1.</li> </ol> <p>I probably reinvented a wheel here..</p> <p>This is my brute foce code, how to make it faster? I've Scipy and Numpy installed, if there's something ready made</p> <pre><code>#cluster center as simple average value def cluster_center(cluster): return sum(cluster) / len(cluster) #Distance between clusters def cluster_distance(a, b): return abs(cluster_center(a) - cluster_center(b)) while True: cluster_distances = [] #If nothing to cluster, ready if len(clusters) &lt; 2: break #Go thru all clusters, calculate shortest distance to neighbor for cluster in clusters: cluster_distances.append((cluster, sorted([(cluster_distance(cluster, c), c) for c in clusters if c != cluster])[0])) #Find out closest pair cluster_distances.sort(cmp=lambda a,b:cmp(a[1], b[1])) #Check if distance is under threshold 15 if cluster_distances[0][1][0] &lt; 15: a = cluster_distances[0][0] b = cluster_distances[0][1][1] #Combine clusters (combine lists) a.extend(b) #Form a new cluster list clusters = [c[0] for c in cluster_distances if c[0] != b] else: break </code></pre>
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    1. USHarriv
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    1. COI wouldn't know where to begin, but luckily someone else might: https://code.google.com/p/scipy-cluster/ and http://glowingpython.blogspot.com/2012/04/k-means-clustering-with-scipy.html
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    2. COK-means clustering requires knowledge of number of clusters, and with scipy-cluster I don't know where to begin :)
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    3. CODid you reinvent agglomerative clustering? Have a look at: http://scikit-learn.org/stable/modules/clustering.html#hierarchical-clustering
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