Clustering Algorithms in Data Science

Data Science 255 views Nov 14, 2025 1 min read

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.

Some advanced sections are available for Registered Members

← Previous

Classification Algorithms in Machine Learning

Dimensionality Reduction Techniques