Clustering
An unsupervised learning technique that groups similar data points together without predefined labels, discovering natural structure and segments within datasets based on feature similarity.
Clustering finds groups in data without being told what the groups should be. Unlike classification, where you train with labeled examples, clustering algorithms discover structure autonomously. K-means (partition data into k groups by minimizing within-cluster distance), DBSCAN (density-based clustering that finds arbitrarily shaped clusters), and hierarchical clustering (building a tree of nested clusters) are the most common approaches.
The choice of clustering algorithm depends on your data and goals. K-means is fast and scalable but assumes spherical clusters and requires specifying k in advance. DBSCAN handles arbitrary shapes and automatically determines the number of clusters but is sensitive to density parameters. Hierarchical clustering provides a multi-scale view but is computationally expensive for large datasets.
For growth teams, clustering is powerful for customer segmentation (discovering behavioral groups), content categorization (automatically grouping similar items), anomaly detection (points that don't belong to any cluster are potential outliers), and market research (finding natural segments in survey or behavioral data). Embedding-based clustering, where you cluster vector representations rather than raw features, has become especially powerful for unstructured data like text and user behavior sequences.
Related Terms
RAG (Retrieval-Augmented Generation)
A technique that grounds LLM responses in external data by retrieving relevant documents at query time and injecting them into the prompt context.
Embeddings
Dense vector representations of text, images, or other data that capture semantic meaning in a high-dimensional space, enabling similarity search and clustering.
Vector Database
A specialized database optimized for storing, indexing, and querying high-dimensional vector embeddings with sub-millisecond similarity search.
LLM (Large Language Model)
A neural network trained on massive text corpora that can generate, understand, and transform natural language for tasks like summarization, classification, and conversation.
Fine-Tuning
The process of further training a pre-trained LLM on a domain-specific dataset to specialize its behavior, style, or knowledge for a particular task.
Prompt Engineering
The practice of designing and iterating on LLM input instructions to reliably produce desired outputs for a specific task.