ClusteringCoordinator Documentation

Overview

ClusteringCoordinator is a coordination layer that manages the clustering workflow, handling data transformation, storage operations, and the clustering process. It acts as an intermediary between your application's data models and the KMeansClusteringService.

Class Structure

ClusteringCoordinator

class ClusteringCoordinator {
    init(storageProvider: StorageProvider, k: Int = 3, vectorDimension: Int = 256)
}

Dependencies:

• storageProvider: Handles persistence of entries and clusters

• clusteringService: Instance of KMeansClusteringService

• vectorDimension: Expected dimension of feature vectors (default: 256)

Main Method

performClustering(threshold:)

func performClustering(threshold: Float) async throws

Orchestrates the complete clustering workflow.

Parameters:

• threshold: Float value determining cluster boundary conditions

Process Flow:

1. Fetches entries from storage

2. Converts entries to feature vectors

3. Performs k-means clustering

4. Transforms results back to application models

5. Persists results to storage

Throws:

• ClusteringError.noValidPoints: No valid points for clustering

• ClusteringError.missingEmbedding: Entry lacks required embedding data

• ClusteringError.invalidVectorDimension: Vector dimension mismatch

Helper Methods

convertEntryToVector(_:)

private func convertEntryToVector(_ entry: Entry) throws -> [Float]

Converts application Entry model to feature vector format.

Parameters:

• entry: The Entry object to convert

Returns:

• [Float]: Feature vector representation

Throws:

• ClusteringError.missingEmbedding

• ClusteringError.invalidVectorDimension

analyzeCluster(entries:points:)

private func analyzeCluster(entries: [Entry], points: [KMeansClusteringService.Point]) -> [String: Any]

Performs analysis on clustered data.

Parameters:

• entries: Array of Entry objects in cluster

• points: Array of Point objects from clustering

Returns:

Dictionary containing:

• dominantSentiment: Most common sentiment in cluster

• timeRange: Temporal bounds of cluster data

• clusterDensity: Measure of point distribution

calculateDominantSentiment(_:)

private func calculateDominantSentiment(_ sentiments: [String]) -> String

Determines most frequent sentiment in a collection.

calculateTimeRange(_:)

private func calculateTimeRange(_ entries: [Entry]) -> (start: Date, end: Date)?

Computes temporal bounds of cluster entries.

calculateClusterDensity(_:)

private func calculateClusterDensity(_ points: [KMeansClusteringService.Point]) -> Float

Calculates average distance between points in cluster.

Error Handling

enum ClusteringError: Error {
    case noValidPoints      // No valid points available for clustering
    case missingEmbedding   // Entry lacks required embedding data
    case invalidVectorDimension  // Vector dimension doesn't match expected size
}

Usage Example

// Initialize coordinator
let coordinator = ClusteringCoordinator(
    storageProvider: myStorageProvider,
    k: 3,
    vectorDimension: 256
)

// Perform clustering
do {
    try await coordinator.performClustering(threshold: 0.5)
} catch let error as ClusteringError {
    switch error {
    case .noValidPoints:
        // Handle no valid points
    case .missingEmbedding:
        // Handle missing embedding
    case .invalidVectorDimension:
        // Handle dimension mismatch
    }
} catch {
    // Handle other errors
}

Integration Points

Storage Integration

Works with StorageProvider to:

• Fetch entries for clustering

• Persist resulting clusters

• Update existing clusters

KMeansClusteringService Integration

• Transforms data into required format

• Handles vector operations

• Processes clustering results

Data Model Integration

Works with:

• Entry models (input)

• Cluster models (output)

• Vector embeddings

• Metadata attributes

Best Practices

1. Error Handling

   • Always use try-catch blocks

   • Handle specific ClusteringError cases

   • Validate data before processing

2. Performance

   • Consider batch sizes for large datasets

   • Monitor memory usage with large vectors

   • Cache results when appropriate

3. Data Validation

   • Verify vector dimensions

   • Validate entry data completeness

   • Check threshold values

Limitations

1. Synchronous vector conversion

2. Fixed vector dimensionality

3. Single storage provider

4. Basic sentiment analysis

Future Improvements

1. Batch processing for large datasets

2. Async vector conversion

3. Multiple storage provider support

4. Enhanced cluster analysis

5. Improved error recovery

6. Performance monitoring

7. Configuration management