Introduction to Clustering Algorithms

In the realm of Machine Learning and Data Science, Clustering Algorithms play a vital role in discovering hidden patterns within data. Unlike supervised learning, clustering is a type of unsupervised learning, where the algorithm automatically groups similar data points based on their characteristics — without using predefined labels.

 What is Clustering?

Clustering is the process of dividing a dataset into groups (called clusters) where data points in the same cluster are more similar to each other than to those in other clusters. It helps in identifying underlying structures and relationships within large datasets.

⚙️ Popular Clustering Algorithms

1. K-Means Clustering – One of the most common algorithms that partitions data into K clusters based on distance measures like Euclidean distance.
2. Hierarchical Clustering – Builds a hierarchy of clusters either by combining smaller clusters (agglomerative) or dividing larger ones (divisive).
3. DBSCAN (Density-Based Spatial Clustering of Applications with Noise) – Groups together closely packed points and marks outliers that lie alone in low-density regions.
4. Mean Shift Clustering – Finds clusters by locating dense areas of data points in the feature space.

🌍 Applications of Clustering

• Market segmentation and customer profiling
• Image compression and pattern recognition
• Document or news categorization
• Environmental data analysis and anomaly detection

By applying clustering, organizations and researchers can better understand data distribution, detect patterns, and make data-driven decisions without prior knowledge of output categories.📘 Full Document Attached Below:

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