Randomstate and kmeans issues

Question

I try to cluster a dataframe of 227 rows in 5 clusters using kmeans algorithm. Each time I run my code I got different labels and different clusters which make my analysis afterwards a bit tricky.

Someone told me to use the parameter: randomstate to have a reproductility in my results. I did. I have the same clusters but still not the same label. Is it normal? Is there a way to get the same labels ?

below my code:

Test sur 5 clusters

# Data
X = df.iloc[:,1:]
myseed = 10

# Modèle kmeans à 5 clusters
km = KMeans(n_clusters=5, random_state=myseed, n_init=30) 

# Fitting du modèle aux points 
km = km.fit(X)
y_km = km.predict(X)

Answer 1

Unfortunately, there isn't a built-in option to do it. Each time you run K-Means, the labels are assigned randomly. Even if you state the same random seed. However, based on this answer in StackOverFlow, you can create a lookup table and run it after your K-Means.

from sklearn.cluster import KMeans
k = 5
kmeans = KMeans(n_clusters=k, random_state=0).fit(X)

idx = np.argsort(kmeans.cluster_centers_.sum(axis=1))
lut = np.zeros_like(idx)
lut[idx] = np.arange

With this, you will always have the same output as

In [73]: kmeans.labels_
Out[73]: array([1, 4, 1, ..., 0, 1, 0])

In [74]: lut[kmeans.labels_]
Out[74]: array([3, 0, 3, ..., 2, 4, 2], dtype=int64

When lut[kmeans.labels_] = 0, then you always have the smallest cluster and with lut[kmeans.labels_] = 4 you have the largest cluster.