Clusters
Clustering in Mixpeek serves as the multimodal equivalent of SQL GROUP BY operations, allowing you to group similar documents together based on feature similarity rather than exact field matches.
Key Concepts
Vector-Based Clustering
- Semantic similarity grouping
- Embedding-based clustering
- Advanced algorithms (HDBSCAN, K-Means)
Attribute-Based Grouping
- Metadata-based organization
- Time-based grouping
- Custom field clustering
Overview
Clustering enables you to organize and group documents based on their feature similarity. Unlike traditional SQL GROUP BY operations that group rows based on exact field matches, clustering uses similarity metrics to group documents that share similar characteristics.
Clustering Types
Mixpeek supports various clustering approaches through the Grouper interface.
Vector Clustering
Groups documents based on embedding similarity using algorithms like K-means or DBSCAN
- •Perfect for finding visually or semantically similar content
- •Supports multiple clustering algorithms
- •Configurable similarity thresholds
Categorical Clustering
Groups documents based on detected categories, objects, or topics
- •Organize content by subject matter
- •Group by detected objects or entities
- •Support for hierarchical categories
Use Cases
Discover how clustering can help organize and analyze your content.
Content Organization
Automatically organize large collections of documents into logical groups
Similar Content Discovery
Find related content by exploring documents within the same cluster
Batch Processing
Process similar documents together for efficiency
Analytics
Analyze patterns and trends within document clusters
Ready to Get Started?
Start organizing and grouping your multimodal content with Mixpeek clustering today.