def main():
# read the preprocessed data
tfidf_matrix = load_sparse_csr()
vocabulary = load_labels()
# k-means clustering
num_clusters = 6
km = KMeans(n_clusters=num_clusters, n_jobs=-1)
km.fit(tfidf_matrix)
# visualize the generated clusters
visualize_clusters(tfidf_matrix, vocabulary, km)
# -*- coding: utf-8 -*-
__author__ = 'Marco'
from matplotlib import pyplot as plt
from scipy.cluster.hierarchy import dendrogram, linkage
import numpy as np
from scipy.cluster.hierarchy import cophenet
from scipy.spatial.distance import pdist
from preprocessing import load_sparse_csr
X = load_sparse_csr()
# generate the linkage matrix
Z = linkage(X.toarray(),'ward')
# calculate full dendrogram
plt.figure(figsize=(25, 10))
plt.title('Hierarchical Clustering Dendrogram')
plt.xlabel('Job')
plt.ylabel('distance')
dendrogram(
Z,
leaf_rotation=180., # rotates the x axis labels
leaf_font_size=8., # font size for the x axis labels
)
plt.show()
