Crate sklears_clustering 

Clustering algorithms for sklears

This crate provides implementations of clustering algorithms including:

• K-Means clustering with various initialization methods
• X-Means for automatic cluster number selection
• G-Means for Gaussian cluster detection with automatic number selection
• Mini-batch K-Means for large datasets
• Fuzzy C-Means clustering with membership degrees
• DBSCAN (Density-Based Spatial Clustering)
• Incremental DBSCAN for streaming data and large datasets
• HDBSCAN (Hierarchical Density-Based Spatial Clustering)
• OPTICS (Ordering Points To Identify Clustering Structure)
• BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies)
• Hierarchical clustering
• Mean Shift with adaptive bandwidth estimation
• Density Peaks clustering for automatic cluster center detection
• KDE Clustering using kernel density estimation for density-based clustering
• Spectral Clustering
• Gaussian Mixture Models with model selection criteria (AIC, BIC, ICL)
• Dirichlet Process Mixture Models for infinite mixture modeling
• Local Outlier Factor (LOF) for density-based outlier detection
• CURE (Clustering Using REpresentatives) for large datasets with irregular shapes
• ROCK (RObust Clustering using linKs) for categorical data clustering
• Streaming clustering algorithms (Online K-Means, CluStream, Sliding Window K-Means)
• Graph clustering algorithms (Modularity-based, Louvain, Label Propagation, Spectral)
• Evolutionary and bio-inspired clustering algorithms (PSO, GA, ACO, ABC, Differential Evolution)
• Comprehensive validation metrics for clustering evaluation including stability analysis

These implementations leverage scirs2’s cluster module for efficient computation.

Re-exports

pub use birch::BIRCHConfig;

pub use birch::ClusteringFeature;

pub use birch::BIRCH;

pub use cure::CUREConfig;

pub use cure::CUREDistanceMetric;

pub use cure::CUREFitted;

pub use cure::CURE;

pub use dbscan::DBSCANConfig;

pub use dbscan::DBSCAN;

pub use dbscan::NOISE;

pub use density_peaks::DensityPeaks;

pub use density_peaks::DensityPeaksConfig;

pub use density_peaks::DistanceMetric as DensityPeaksDistanceMetric;

pub use dirichlet_process::DirichletProcessConfig;

pub use dirichlet_process::DirichletProcessMixture;

pub use dirichlet_process::PredictProbaDP;

pub use ensemble::BaggingClustering;

pub use ensemble::EnsembleConfig;

pub use ensemble::EnsembleConfigBuilder;

pub use ensemble::EnsembleMethod;

pub use ensemble::EnsembleResult;

pub use ensemble::EvidenceAccumulationClustering;

pub use ensemble::VotingEnsemble;

pub use evolutionary::PSOClustering;

pub use evolutionary::PSOClusteringBuilder;

pub use evolutionary::PSOClusteringFitted;

pub use feature_selection::FeatureSelectionConfig;

pub use feature_selection::FeatureSelectionConfigBuilder;

pub use feature_selection::FeatureSelectionMethod;

pub use feature_selection::FeatureSelectionResult;

pub use feature_selection::FeatureSelector;

pub use fuzzy_cmeans::FuzzyCMeans;

pub use fuzzy_cmeans::FuzzyCMeansConfig;

pub use fuzzy_cmeans::PredictMembership;

pub use gmm::BayesianGaussianMixture;

pub use gmm::CovarianceType;

pub use gmm::GaussianMixture;

pub use gmm::GaussianMixtureConfig;

pub use gmm::ModelSelectionCriterion;

pub use gmm::ModelSelectionResult;

pub use gmm::PredictProba;

pub use gmm::WeightInit;

pub use graph_clustering::Graph;

pub use graph_clustering::GraphClusteringResult;

pub use graph_clustering::LabelPropagationClustering;

pub use graph_clustering::LabelPropagationConfig as GraphLabelPropagationConfig;

pub use graph_clustering::LouvainClustering;

pub use graph_clustering::LouvainConfig;

pub use graph_clustering::LouvainResult;

pub use graph_clustering::ModularityClustering;

pub use graph_clustering::ModularityClusteringConfig;

pub use graph_clustering::SpectralGraphClustering;

pub use graph_clustering::SpectralGraphConfig;

pub use hdbscan::ClusterStat;

pub use hdbscan::HDBSCANConfig;

pub use hdbscan::HDBSCAN;

pub use hierarchical::AgglomerativeClustering;

pub use hierarchical::AgglomerativeClusteringConfig;

pub use hierarchical::Constraint;

pub use hierarchical::ConstraintSet;

pub use hierarchical::Dendrogram;

pub use hierarchical::DendrogramExport;

pub use hierarchical::DendrogramLinkExport;

pub use hierarchical::DendrogramNode;

pub use hierarchical::DendrogramNodeExport;

pub use hierarchical::MemoryStrategy;

pub use incremental_dbscan::DistanceMetric as IncrementalDistanceMetric;

pub use incremental_dbscan::IncrementalDBSCAN;

pub use incremental_dbscan::IncrementalDBSCANConfig;

pub use kde_clustering::BandwidthMethod;

pub use kde_clustering::KDEClustering;

pub use kde_clustering::KDEClusteringConfig;

pub use kde_clustering::KernelType;

pub use kmeans::GMeans;

pub use kmeans::GMeansConfig;

pub use kmeans::InformationCriterion;

pub use kmeans::KMeans;

pub use kmeans::KMeansConfig;

pub use kmeans::KMeansInit;

pub use kmeans::MiniBatchKMeans;

pub use kmeans::MiniBatchKMeansConfig;

pub use kmeans::XMeans;

pub use kmeans::XMeansConfig;

pub use locality_sensitive_hashing::LSHConfig;

pub use locality_sensitive_hashing::LSHFamily;

pub use locality_sensitive_hashing::LSHIndex;

pub use locality_sensitive_hashing::LSHIndexStats;

pub use locality_sensitive_hashing::MemoryUsage;

pub use locality_sensitive_hashing::TableStats;

pub use lof::DistanceMetric as LOFDistanceMetric;

pub use lof::LOFConfig;

pub use lof::LOF;

pub use mean_shift::MeanShift;

pub use mean_shift::MeanShiftConfig;

pub use memory_mapped::MemoryMappedConfig;

pub use memory_mapped::MemoryMappedDistanceMatrix;

pub use memory_mapped::MemoryStats;

pub use multi_view::ConsensusClustering;

pub use multi_view::ConsensusClusteringConfig;

pub use multi_view::ConsensusClusteringFitted;

pub use multi_view::ConsensusMethod;

pub use multi_view::MultiViewData;

pub use multi_view::MultiViewKMeans;

pub use multi_view::MultiViewKMeansConfig;

pub use multi_view::MultiViewKMeansFitted;

pub use multi_view::ViewWeighting;

pub use multi_view::WeightLearning;

pub use optics::Algorithm;

pub use optics::ClusterMethod;

pub use optics::DistanceMetric as OpticsDistanceMetric;

pub use optics::Optics;

pub use optics::OpticsConfig;

pub use optics::OpticsOrdering;

pub use out_of_core::ClusterSummary;

pub use out_of_core::OutOfCoreConfig;

pub use out_of_core::OutOfCoreDataLoader;

pub use out_of_core::OutOfCoreKMeans;

pub use rock::ROCKConfig;

pub use rock::ROCKFitted;

pub use rock::ROCKSimilarity;

pub use rock::ROCK;

pub use semi_supervised::ConstrainedKMeans;

pub use semi_supervised::ConstrainedKMeansConfig;

pub use semi_supervised::ConstrainedKMeansFitted;

pub use semi_supervised::ConstraintHandling;

pub use semi_supervised::ConstraintType;

pub use semi_supervised::LabelPropagation;

pub use semi_supervised::LabelPropagationConfig;

pub use semi_supervised::LabelPropagationFitted;

pub use simd_distances::simd_distance;

pub use simd_distances::simd_distance_batch;

pub use simd_distances::simd_k_nearest_neighbors;

pub use simd_distances::DistanceMetric;

pub use simd_distances::OptimizedDistanceComputer;

pub use simd_distances::SimdDistanceMetric;

pub use sparse_matrix::GraphStats;

pub use sparse_matrix::SparseDistanceMatrix;

pub use sparse_matrix::SparseEntry;

pub use sparse_matrix::SparseMatrixConfig;

pub use sparse_matrix::SparseMatrixStats;

pub use sparse_matrix::SparseNeighborhoodGraph;

pub use spectral::Affinity;

pub use spectral::EigenSolver;

pub use spectral::NormalizationMethod;

pub use spectral::SpectralClustering;

pub use spectral::SpectralClusteringConfig;

pub use streaming::CluStream;

pub use streaming::MicroCluster;

pub use streaming::OnlineKMeans;

pub use streaming::SlidingWindowKMeans;

pub use streaming::StreamingConfig;

pub use text_clustering::DocumentClustering;

pub use text_clustering::DocumentClusteringConfig;

pub use text_clustering::DocumentClusteringResult;

pub use text_clustering::SphericalInit;

pub use text_clustering::SphericalKMeans;

pub use text_clustering::SphericalKMeansConfig;

pub use text_clustering::SphericalKMeansFitted;

pub use time_series::CentroidAveraging;

pub use time_series::ChangeDetectionTest;

pub use time_series::DTWKMeans;

pub use time_series::DTWKMeansConfig;

pub use time_series::DTWKMeansFitted;

pub use time_series::RegimeChangeConfig;

pub use time_series::RegimeChangeDetector;

pub use time_series::RegimeChangeResult;

pub use time_series::ShapeClustering;

pub use time_series::ShapeClusteringConfig;

pub use time_series::ShapeClusteringFitted;

pub use time_series::ShapeDistanceMetric;

pub use time_series::TemporalSegmentationClustering;

pub use time_series::TemporalSegmentationConfig;

pub use time_series::TemporalSegmentationResult;

pub use validation::ClusteringValidator;

pub use validation::GapStatisticResult;

pub use validation::SilhouetteResult;

pub use validation::ValidationMetric;

Modules

birch

BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies) implementation

cure

CURE (Clustering Using REpresentatives) Algorithm

dbscan

DBSCAN (Density-Based Spatial Clustering of Applications with Noise) implementation using scirs2

density_peaks

Density Peaks Clustering algorithm

dirichlet_process

Dirichlet Process Mixture Models

ensemble

Ensemble Clustering Algorithms

evolutionary

Evolutionary and bio-inspired clustering algorithms.

feature_selection

Feature Selection for Clustering

fuzzy_cmeans

Fuzzy C-Means clustering implementation

gmm

Gaussian Mixture Models (GMM)

graph_clustering

Graph Clustering Algorithms

hdbscan

HDBSCAN (Hierarchical Density-Based Spatial Clustering of Applications with Noise) implementation using scirs2

hierarchical

Hierarchical clustering implementation using scirs2

incremental_dbscan

Incremental DBSCAN for Streaming Data

kde_clustering

Kernel Density Estimation (KDE) Clustering

kmeans

K-Means clustering implementations

locality_sensitive_hashing

Locality-Sensitive Hashing (LSH) for approximate distance computations

lof

Local Outlier Factor (LOF) implementation for density-based outlier detection

mean_shift

Mean Shift Clustering

memory_mapped

Memory-mapped distance matrix computation for large datasets

multi_view

Multi-View Clustering Algorithms

optics

OPTICS (Ordering Points To Identify Clustering Structure) implementation

out_of_core

Out-of-Core Clustering Algorithms

performance

Performance optimizations for clustering algorithms

prelude

Prelude module for convenient imports

rock

ROCK (RObust Clustering using linKs) Algorithm

semi_supervised

Semi-Supervised Clustering Algorithms

simd_distances

SIMD-optimized distance computations for clustering algorithms

sparse_matrix

Sparse matrix representations for large-scale clustering

spectral

Spectral Clustering

streaming

Streaming Clustering Algorithms

text_clustering

Text and High-Dimensional Clustering Algorithms

time_series

Time Series Clustering Algorithms

validation

Comprehensive Clustering Validation Framework

Enums

DensityDistanceMetric

Distance metric enumeration for clustering algorithms

LinkageMethod

Linkage methods for hierarchical clustering

Metric

Distance metrics for hierarchical clustering