Clustering

Clustering is a type of unsupervised learning where the goal is to group similar data points together based on their features. Unlike supervised learning, clustering does not require labeled data. Instead, it identifies patterns and structures within the data itself.

Common use cases for clustering include:

• Customer Segmentation: Grouping customers based on purchasing behavior for targeted marketing.
• Anomaly Detection: Identifying unusual data points that deviate from the norm, such as fraud detection in financial transactions.
• Document Clustering: Organizing a large set of documents into topics or themes.

Main algorithms used for clustering tasks include:

• K-Means Clustering
• Hierarchical Clustering
• DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
• Gaussian Mixture Models
• Mean Shift Clustering
• Spectral Clustering