Welcome to the World of Clustering! 🐸

Introduction to Clustering

Clustering is a powerful technique used in machine learning to group similar data points together. It's one of the key unsupervised learning techniques and is commonly used for data analysis and pattern recognition.

Types of Clustering Algorithms

• K-Means Clustering: One of the most popular clustering algorithms that partitions data into K distinct clusters.
• Hierarchical Clustering: Builds a tree of clusters and is often used for large datasets with unknown structures.
• DBSCAN (Density-Based Spatial Clustering of Applications with Noise): Great for identifying clusters of varying shapes in data with a lot of noise.

Applications of Clustering

Clustering has a variety of applications such as:

• Market segmentation
• Social network analysis
• Anomaly detection
• Image segmentation
• Group similar news articles

Interactive Clustering Form

Explore More

Interested in diving deeper? Check out our other tutorials:

• Supervised Learning
• Neural Networks
• Reinforcement Learning