from sklearn.cluster import KMeans
cs = []
for i in range(1, 11):
kmeans = KMeans(n_clusters = i, init = 'k-means++', max_iter = 300, n_init = 10, random_state = 0)
kmeans.fit(X)
cs.append(kmeans.inertia_)
plt.plot(range(1, 11), cs)
plt.title('The Elbow Method')
plt.xlabel('Number of clusters')
plt.ylabel('CS')
plt.show()
when I run this code, I have the following error:
AttributeError: 'NoneType' object has no attribute 'split'
Your scripts working but I assume that your X is not valid if you see my comment under your post.
You can try following based one "Understanding Distortion and Inertia in K-Means Clustering"
So let's generate quickly synthetic data with valid X values:
import matplotlib.pyplot as plt
from sklearn.datasets import make_blobs
from sklearn.cluster import KMeans
# Generate synthetic dataset
X, _ = make_blobs(n_samples=300, centers=4, cluster_std=1.0, random_state=42)
# Initialize variables for the elbow method
K = range(1, 11)
distortions = []
inertias = []
# Calculate distortions and inertias for different numbers of clusters
for k in K:
kmeans = KMeans(n_clusters=k, init='k-means++', max_iter=300, n_init=10, random_state=0)
kmeans.fit(X)
distortions.append(kmeans.inertia_)
inertias.append(kmeans.inertia_)
# Cluster the data for visualization
optimal_k = 4 # Assume 4 clusters for visualization
kmeans_optimal = KMeans(n_clusters=optimal_k, init='k-means++', max_iter=300, n_init=10, random_state=0)
y_kmeans = kmeans_optimal.fit_predict(X)
# Create a 2x2 layout with bottom two subplots merged for better visibility
fig = plt.figure(figsize=(12, 10))
# Plot 1: Original Data
ax1 = fig.add_subplot(2, 2, 1)
ax1.scatter(X[:, 0], X[:, 1], s=50)
ax1.set_title("Original Data")
ax1.grid(True)
# Plot 2: Clustered Data
ax2 = fig.add_subplot(2, 2, 2)
ax2.scatter(X[:, 0], X[:, 1], c=y_kmeans, s=50, cmap='viridis')
ax2.scatter(kmeans_optimal.cluster_centers_[:, 0], kmeans_optimal.cluster_centers_[:, 1],
s=200, c='red', marker='X', label='Centroids')
ax2.legend()
ax2.set_title(f"Clustered Data with {optimal_k} Clusters")
ax2.grid(True)
# Plot 3: Combined Elbow Method (Distortion and Inertia)
ax3 = fig.add_subplot(2, 1, 2)
ax3.plot(K, distortions, 'bx-', label='Distortion (Inertia)')
ax3.plot(K, inertias, 'go-', label='Inertia', markerfacecolor='none')
ax3.axvline(x=4, color='red', linestyle='--', label='Optimal k=4')
ax3.set_xlabel('Number of Clusters (k)')
ax3.set_ylabel('Value\n Within-Cluster Sum of Squares (WCSS)')
ax3.set_title('The Elbow Method (Distortion and Inertia)')
ax3.legend()
ax3.grid(True)
# Adjust layout and show the plots
plt.tight_layout()
plt.show()
range(1,11)as argument toplot. It should belist(range(1,11)).X?splitattribute is used internally byKMeansto process data. IfXisNoneor has invalid contents,KMeansmay attempt operations onNoneTypevalues, resulting in the error. Fixing the input data (X) should resolve the error. If you encounter further issues, share the full code or dataset for more meaningful debugging! Your problem is very elementary.