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    <p>Here is an approach based on medoids. First install MlPy. On Ubuntu</p> <pre><code>sudo apt-get install python-mlpy </code></pre> <p>Then</p> <pre><code>import numpy as np import mlpy class distance: def compute(self, s1, s2): l1 = len(s1) l2 = len(s2) matrix = [range(zz,zz + l1 + 1) for zz in xrange(l2 + 1)] for zz in xrange(0,l2): for sz in xrange(0,l1): if s1[sz] == s2[zz]: matrix[zz+1][sz+1] = min(matrix[zz+1][sz] + 1, matrix[zz][sz+1] + 1, matrix[zz][sz]) else: matrix[zz+1][sz+1] = min(matrix[zz+1][sz] + 1, matrix[zz][sz+1] + 1, matrix[zz][sz] + 1) return matrix[l2][l1] x = np.array(['ape', 'appel', 'apple', 'peach', 'puppy']) km = mlpy.Kmedoids(k=3, dist=distance()) medoids,clusters,a,b = km.compute(x) print medoids print clusters print a print x[medoids] for i,c in enumerate(x[medoids]): print "medoid", c print x[clusters[a==i]] </code></pre> <p>The output is</p> <pre><code>[4 3 1] [0 2] [2 2] ['puppy' 'peach' 'appel'] medoid puppy [] medoid peach [] medoid appel ['ape' 'apple'] </code></pre> <p>The bigger word list and using k=10</p> <pre><code>medoid he ['or' 'his' 'my' 'have' 'if' 'year' 'of' 'who' 'us' 'use' 'people' 'see' 'make' 'be' 'up' 'we' 'the' 'one' 'her' 'by' 'it' 'him' 'she' 'me' 'over' 'after' 'get' 'what' 'I'] medoid out ['just' 'only' 'your' 'you' 'could' 'our' 'most' 'first' 'would' 'but' 'about'] medoid to ['from' 'go' 'its' 'do' 'into' 'so' 'for' 'also' 'no' 'two'] medoid now ['new' 'how' 'know' 'not'] medoid time ['like' 'take' 'come' 'some' 'give'] medoid because [] medoid an ['want' 'on' 'in' 'back' 'say' 'and' 'a' 'all' 'can' 'as' 'way' 'at' 'day' 'any'] medoid look ['work' 'good'] medoid will ['with' 'well' 'which'] medoid then ['think' 'that' 'these' 'even' 'their' 'when' 'other' 'this' 'they' 'there' 'than' 'them'] </code></pre>
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    1. POFuzzy Group By, Grouping Similar Words
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    1. COinteresting i had no comments or votes on this answer. hmm..
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