9. Develop a program to implement k-means clustering using Wisconsin Breast Cancer data set and visualize the clustering result.
PROGRAM:
# install required packages
#pip install pandas matplotlib scikit-learn
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.datasets import load_breast_cancer
from sklearn.preprocessing import StandardScaler
from sklearn.cluster import KMeans
from sklearn.decomposition import PCA
from sklearn.metrics import confusion_matrix, accuracy_score
# ==============================
# 1. Load Dataset
# ==============================
cancer = load_breast_cancer()
X = cancer.data
y = cancer.target
df = pd.DataFrame(X, columns=cancer.feature_names)
print("Dataset Preview:")
print(df.head())
print("\nTarget Names:")
print(cancer.target_names)
# ==============================
# 2. Standardize Features
# ==============================
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
# ==============================
# 3. Apply K-Means Clustering
# ==============================
kmeans = KMeans(n_clusters=2, random_state=42, n_init=10)
clusters = kmeans.fit_predict(X_scaled)
# ==============================
# 4. Reduce Dimensions using PCA for Visualization
# ==============================
pca = PCA(n_components=2)
X_pca = pca.fit_transform(X_scaled)
pca_df = pd.DataFrame(X_pca, columns=["PC1", "PC2"])
pca_df["Cluster"] = clusters
pca_df["Actual"] = y
print("\nCluster Results:")
print(pca_df.head())
# ==============================
# 5. Visualize K-Means Clustering Result
# ==============================
plt.figure(figsize=(8, 6))
plt.scatter(
pca_df["PC1"],
pca_df["PC2"],
c=pca_df["Cluster"],
cmap="viridis",
edgecolor="black"
)
plt.xlabel("Principal Component 1")
plt.ylabel("Principal Component 2")
plt.title("K-Means Clustering on Wisconsin Breast Cancer Dataset")
plt.colorbar(label="Cluster")
plt.grid(True)
plt.show()
# ==============================
# 6. Compare with Actual Classes
# ==============================
plt.figure(figsize=(8, 6))
plt.scatter(
pca_df["PC1"],
pca_df["PC2"],
c=pca_df["Actual"],
cmap="coolwarm",
edgecolor="black"
)
plt.xlabel("Principal Component 1")
plt.ylabel("Principal Component 2")
plt.title("Actual Classes of Wisconsin Breast Cancer Dataset")
plt.colorbar(label="Actual Class")
plt.grid(True)
plt.show()
# ==============================
# 7. Optional Evaluation
# ==============================
print("\nConfusion Matrix:")
print(confusion_matrix(y, clusters))
# Since cluster labels may be reversed, calculate both accuracies
accuracy1 = accuracy_score(y, clusters)
accuracy2 = accuracy_score(y, 1 - clusters)
print("\nK-Means Clustering Accuracy:")
print(max(accuracy1, accuracy2))OUTPUT:
Dataset Preview:
mean radius mean texture mean perimeter mean area ... worst concavity worst concave points worst symmetry worst fractal dimension
0 17.99 10.38 122.80 1001.0 ... 0.7119 0.2654 0.4601 0.11890
1 20.57 17.77 132.90 1326.0 ... 0.2416 0.1860 0.2750 0.08902
2 19.69 21.25 130.00 1203.0 ... 0.4504 0.2430 0.3613 0.08758
3 11.42 20.38 77.58 386.1 ... 0.6869 0.2575 0.6638 0.17300
4 20.29 14.34 135.10 1297.0 ... 0.4000 0.1625 0.2364 0.07678
[5 rows x 30 columns]
Target Names:
['malignant' 'benign']
Cluster Results:
PC1 PC2 Cluster Actual
0 9.192837 1.948583 1 0
1 2.387802 -3.768172 1 0
2 5.733896 -1.075174 1 0
3 7.122953 10.275589 1 0
4 3.935302 -1.948072 1 0
Confusion Matrix:
[[ 36 176]
[339 18]]
K-Means Clustering Accuracy:
0.9050966608084359
