Sklearn BIC criterion : differents optimum values of k for clustering - python-3.x

Question

I want to determine the best value of k (number of clusters) for the KMeans algo and a dataset.
I found a ressource in the documentation of Sklearn : The Gaussian Mixture Model Selection using the BIC criterion.
I found an example of code on the site that I adapted to my dataset.
But each run of this code give a different value of optimal value of k . Why ?
Here the code :
import numpy as np
import pandas as pd
import itertools
from scipy import linalg
import matplotlib.pyplot as plt
import matplotlib as mpl
from sklearn import mixture
print(__doc__)
# Number of samples per component
n_samples = 440
path = 'C:/Users/Lionel/Downloads'
file = 'Wholesale customers data.csv'
data = pd.read_csv(path + '/'+file)
X = np.array(data.iloc[:,2 :])
lowest_bic = np.infty
bic = []
n_components_range = range(1, 12)
cv_types = ['spherical', 'tied', 'diag', 'full']
for cv_type in cv_types:
for n_components in n_components_range:
# Fit a Gaussian mixture with EM
gmm = mixture.GaussianMixture(n_components=n_components,
covariance_type=cv_type)
gmm.fit(X)
bic.append(gmm.bic(X))
if bic[-1] < lowest_bic:
lowest_bic = bic[-1]
best_gmm = gmm
bic = np.array(bic)
color_iter = itertools.cycle(['navy', 'turquoise', 'cornflowerblue',
'darkorange'])
clf = best_gmm
print(clf)
bars = []
# Plot the BIC scores
spl = plt.subplot(2, 1, 1)
#spl = plt.plot()
for i, (cv_type, color) in enumerate(zip(cv_types, color_iter)):
xpos = np.array(n_components_range) + .2 * (i - 2)
bars.append(plt.bar(xpos, bic[i * len(n_components_range):
(i + 1) * len(n_components_range)],
width=.2, color=color))
plt.xticks(n_components_range)
plt.ylim([bic.min() * 1.01 - .01 * bic.max(), bic.max()])
plt.title('BIC score per model')
xpos = np.mod(bic.argmin(), len(n_components_range)) + .65 +\
.2 * np.floor(bic.argmin() / len(n_components_range))
plt.text(xpos, bic.min() * 0.97 + .03 * bic.max(), '*', fontsize=14)
spl.set_xlabel('Number of components')
spl.legend([b[0] for b in bars], cv_types)
# Plot the winner
splot = plt.subplot(2, 1, 2)
Y_ = clf.predict(X)
for i, (mean, cov, color) in enumerate(zip(clf.means_, clf.covariances_,
color_iter)):
v, w = linalg.eigh(cov)
if not np.any(Y_ == i):
continue
plt.scatter(X[Y_ == i, 0], X[Y_ == i, 1], .8, color=color)
# Plot an ellipse to show the Gaussian component
angle = np.arctan2(w[0][1], w[0][0])
angle = 180. * angle / np.pi # convert to degrees
v = 2. * np.sqrt(2.) * np.sqrt(v)
ell = mpl.patches.Ellipse(mean, v[0], v[1], 180. + angle, color=color)
ell.set_clip_box(splot.bbox)
ell.set_alpha(.5)
splot.add_artist(ell)
plt.xticks(())
plt.yticks(())
plt.title('Selected GMM: full model, 2 components')
plt.subplots_adjust(hspace=.35, bottom=.02)
plt.show()
Here the link to my dataset :
https://drive.google.com/open?id=1yMw1rMh12ml6Lh3yrL6WDLbEnLM-SmiN
Have you an explanation for this behaviour ?