test: Add comprehensive integration tests for Clustering module [P0]

[ooples]

Overview

This issue tracks the implementation of comprehensive integration tests for the Clustering module. This is a P0 (Critical Priority) module for unsupervised learning.

Parent Issue: #615

Current Status

• Coverage: 0% for algorithms (only metrics tested)
• Source Files: 90+ files
• Test Files: Only ClusteringMetricsIntegrationTests.cs (tests metrics, not algorithms)

Module Location

src/Clustering/

Classes to Test

Core Clustering Algorithms

Partitional Clustering:

• KMeans
• MiniBatchKMeans
• OnlineKMeans
• KMedoids
• FuzzyCMeans
• GMeans
• XMeans
• BisectingKMeans
• SeededKMeans
• COPKMeans (constrained)

Density-Based Clustering:

• DBSCAN
• HDBSCAN
• OPTICS
• Denclue
• MeanShift

Hierarchical Clustering:

• AgglomerativeClustering
• BIRCH
• CURE
• CLARANS

Spectral Clustering:

• SpectralClustering

Subspace Clustering:

• CLIQUE
• SUBCLU

Probabilistic Clustering:

• GaussianMixtureModel
• AffinityPropagation

Other:

• SelfOrganizingMap
• ConsensusClustering

Distance Metrics

• EuclideanDistance
• ManhattanDistance
• CosineDistance
• MahalanobisDistance
• ChebyshevDistance
• MinkowskiDistance

Spatial Data Structures

• KDTree
• BallTree

Evaluation Metrics (already partially tested)

• SilhouetteScore
• DaviesBouldinIndex
• CalinskiHarabaszIndex
• DunnIndex
• AdjustedRandIndex
• NormalizedMutualInformation
• VMeasure
• FMeasure
• FowlkesMallowsIndex
• JaccardIndex
• Purity
• VariationOfInformation
• ClusteringEntropy
• ConnectivityIndex
• WCSS

Validation Methods

• ElbowMethod
• GapStatistic
• StabilityValidation
• BootstrapValidation

AutoML

• ClusteringAutoML
• ClusteringGridSearch
• ClusteringEvaluator

Test Categories Required

1. Basic Clustering Tests

• Verify clusters are assigned for all data points
• Test with known cluster structures (blobs, moons, circles)
• Verify number of clusters matches expectation
• Test reproducibility with same random seed

2. Algorithm-Specific Tests

KMeans Family:

• Test convergence behavior
• Verify centroid computation
• Test with different initialization methods (random, k-means++)
• Test with different distance metrics
• Compare against scikit-learn KMeans

DBSCAN:

• Test eps and min_samples parameters
• Verify noise point detection
• Test with varying density clusters
• Compare against scikit-learn DBSCAN

Hierarchical:

• Test different linkage methods (single, complete, average, ward)
• Verify dendrogram structure
• Test distance thresholds

Spectral:

• Test different affinity matrices
• Verify eigenvalue computation
• Test with different number of components

3. Edge Cases

• Test with single data point
• Test with all identical points
• Test with high-dimensional data
• Test with outliers
• Test with very small/large values
• Test with NaN values (should handle gracefully)

4. Performance Tests

• Test with varying dataset sizes
• Verify memory usage is reasonable
• Test convergence speed

5. Serialization Tests

• Save and load trained clustering models
• Verify predictions match after reload
• Test with various configurations

6. Clone Tests

• Clone trained models
• Verify independence
• Verify predictions match

Mathematical Correctness Verification

KMeans

• Verify WCSS (Within-Cluster Sum of Squares) decreases with iterations
• Compare centroids against manually computed values
• Verify cluster assignments minimize distance to centroid

DBSCAN

• Verify core points, border points, noise classification
• Test reachability relationships

GMM

• Verify log-likelihood increases with iterations
• Test probability assignments sum to 1

Metrics

• Verify Silhouette scores in range [-1, 1]
• Verify Davies-Bouldin lower is better
• Compare against scikit-learn metrics

Test Data

Create test datasets:

1. Well-separated blobs - 3 clusters, clearly separable
2. Overlapping clusters - 2 clusters with overlap
3. Non-spherical clusters - Moons, circles
4. High-dimensional - 50+ dimensions
5. Imbalanced clusters - Different sizes

Priority Order

1. Critical (Test First):

   • KMeans
   • DBSCAN
   • GaussianMixtureModel
   • AgglomerativeClustering

2. High:

   • MiniBatchKMeans
   • HDBSCAN
   • SpectralClustering
   • KMedoids
   • MeanShift

3. Medium:

   • All other algorithms
   • Validation methods
   • AutoML components

Acceptance Criteria

• All major clustering algorithms have tests
• Tests cover basic functionality and edge cases
• Mathematical correctness verified against reference implementations
• Serialization works correctly
• At least 80% code coverage
• All tests pass on both net8.0 and net471

References

• scikit-learn clustering: https://scikit-learn.org/stable/modules/clustering.html
• HDBSCAN docs: https://hdbscan.readthedocs.io/

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🔗 Similar Issues

Related Issues

• test: Add comprehensive integration tests for NeuralNetworks module [P0] #616
• Epic: Comprehensive Integration Test Coverage for All Modules #615
• [Phase 2 Parity] Implement Comprehensive Clustering Algorithms #382
• [Test Coverage] Implement Comprehensive Integration Tests for Regression Module #611
• test: comprehensive integration tests for Classification module #612

🔗 Related PRs

#259 - feat: Production-Ready Multi-Class Confusion Matrix (Issue #257) [merged]
#577 - feat(clustering): implement comprehensive clustering infrastructure (#382) [merged]
#600 - test(statistics): add comprehensive statistics integration tests [merged]
#608 - test: add comprehensive integration tests for fit detectors [merged]
#610 - test: add integration tests for transfer learning (#360, #361) [closed]

👤 Suggested Assignees

• ooples

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