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    <p>Here is another version using Affinity Propagation algorithm. </p> <pre><code>import numpy as np import scipy.linalg as lin import Levenshtein as leven import matplotlib.pyplot as plt from sklearn.cluster import KMeans from sklearn.cluster import AffinityPropagation import itertools words = np.array( ['the', 'be', 'to', 'of', 'and', 'a', 'in', 'that', 'have', 'I', 'it', 'for', 'not', 'on', 'with', 'he', 'as', 'you', 'do', 'at', 'this', 'but', 'his', 'by', 'from', 'they', 'we', 'say', 'her', 'she', 'or', 'an', 'will', 'my', 'one', 'all', 'would', 'there', 'their', 'what', 'so', 'up', 'out', 'if', 'about', 'who', 'get', 'which', 'go', 'me', 'when', 'make', 'can', 'like', 'time', 'no', 'just', 'him', 'know', 'take', 'people', 'into', 'year', 'your', 'good', 'some', 'could', 'them', 'see', 'other', 'than', 'then', 'now', 'look', 'only', 'come', 'its', 'over', 'think', 'also', 'back', 'after', 'use', 'two', 'how', 'our', 'work', 'first', 'well', 'way', 'even', 'new', 'want', 'because', 'any', 'these', 'give', 'day', 'most', 'us']) print "calculating distances..." (dim,) = words.shape f = lambda (x,y): -leven.distance(x,y) res=np.fromiter(itertools.imap(f, itertools.product(words, words)), dtype=np.uint8) A = np.reshape(res,(dim,dim)) af = AffinityPropagation().fit(A) cluster_centers_indices = af.cluster_centers_indices_ labels = af.labels_ unique_labels = set(labels) for i in unique_labels: print words[labels==i] </code></pre> <p>Distances had to be converted to similarities, I did that by taking the negative of distance. The output is</p> <pre><code>['to' 'you' 'do' 'by' 'so' 'who' 'go' 'into' 'also' 'two'] ['it' 'with' 'at' 'if' 'get' 'its' 'first'] ['of' 'for' 'from' 'or' 'your' 'look' 'after' 'work'] ['the' 'be' 'have' 'I' 'he' 'we' 'her' 'she' 'me' 'give'] ['this' 'his' 'which' 'him'] ['and' 'a' 'in' 'an' 'my' 'all' 'can' 'any'] ['on' 'one' 'good' 'some' 'see' 'only' 'come' 'over'] ['would' 'could'] ['but' 'out' 'about' 'our' 'most'] ['make' 'like' 'time' 'take' 'back'] ['that' 'they' 'there' 'their' 'when' 'them' 'other' 'than' 'then' 'think' 'even' 'these'] ['not' 'no' 'know' 'now' 'how' 'new'] ['will' 'people' 'year' 'well'] ['say' 'what' 'way' 'want' 'day'] ['because'] ['as' 'up' 'just' 'use' 'us'] </code></pre>
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