MajorClust algorithm implementation using sklearn based on SO conversation about text clustering using python(http://stackoverflow.com/questions/1789254/clustering-text-in-python).

MajorClust.py

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import linear_kernel
import numpy as np
from itertools import combinations
from random import shuffle

def majorclust_sklearn():    
    texts = [
        "foo blub baz",
        "foo bar baz",
        "asdf bsdf csdf",
        "foo bab blub",
        "csdf hddf kjtz",
        "123 456 890",
        "321 890 456 foo",
        "123 890 uiop",
    ]
    vectorizer = TfidfVectorizer()
    corpus_mat = vectorizer.fit_transform(texts)
    num_of_samples, num_of_features = corpus_mat.shape

    cosine_distances = np.zeros((num_of_samples, num_of_samples))
    for i in range(len(texts)):
        cosine_distances[i] = linear_kernel(corpus_mat[i:i+1], corpus_mat).flatten()
        cosine_distances[i, i] = 0

    t = False
    indices = np.arange(num_of_samples)
    while not t:
        t = True
        shuffled_indices = np.arange(num_of_samples)
        shuffle(shuffled_indices)
        for index in shuffled_indices:
            # aggregating edge weights 
            new_index = np.argmax(np.bincount(indices, 
                                              weights=cosine_distances[index]))
            if indices[new_index] != indices[index]:
                indices[index] = indices[new_index]
                t = False

    clusters = {}
    for index, target in enumerate(indices):
        clusters.setdefault(target, []).append(texts[index])

    for cluster in clusters:
        print(80*"=")
        print("\n".join(clusters[cluster]))
    return clusters