Clustering Algorithms in Data Science

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Introduction

Clustering is an unsupervised learning method used to group similar data points into clusters. It is widely used in segmentation, pattern discovery, and anomaly detection.

1. K-Means Clustering

K-Means groups data into K clusters using Euclidean distance.

from sklearn.cluster import KMeans

model = KMeans(n_clusters=3)
model.fit(X)
labels = model.labels_
  

2. Hierarchical Clustering

Creates a tree-like structure (dendrogram) to visualize cluster formation.

from scipy.cluster.hierarchy import dendrogram, linkage

linkage_matrix = linkage(X, method="ward")
  

3. DBSCAN (Density-Based)

Groups points based on density and identifies noise/outliers.

from sklearn.cluster import DBSCAN

model = DBSCAN(eps=0.3, min_samples=5)
labels = model.fit_predict(X)
  

4. Use Cases

• Customer Segmentation
• Anomaly Detection
• Market Basket Analysis
• Image Segmentation

Conclusion

Clustering is essential for discovering hidden patterns in data. Understanding each algorithm helps you choose the right technique based on data behavior and noise levels.