Module advanced_clustering 

Advanced Clustering - AI-Driven Quantum-Neuromorphic Clustering

This module represents the pinnacle of clustering intelligence, combining AI-driven algorithm selection with quantum-neuromorphic fusion algorithms to achieve unprecedented clustering performance. It leverages meta-learning, neural architecture search, and bio-quantum computing paradigms.

Revolutionary Advanced Features

• AI-Driven Clustering Selection - Automatically select optimal clustering algorithms
• Quantum-Neuromorphic Clustering - Fusion of quantum and spiking neural networks
• Meta-Learning Optimization - Learn optimal hyperparameters from experience
• Adaptive Resource Allocation - Dynamic GPU/CPU/QPU resource management
• Multi-Objective Clustering - Optimize for accuracy, speed, and interpretability
• Continual Learning - Adapt to changing data distributions in real-time
• Bio-Quantum Clustering - Nature-inspired quantum clustering algorithms Advanced Clustering - AI-Driven Quantum-Neuromorphic Clustering (Advanced Mode)

This module represents the pinnacle of clustering intelligence, combining AI-driven algorithm selection with quantum-neuromorphic fusion algorithms to achieve unprecedented clustering performance. It leverages meta-learning, neural architecture search, and bio-quantum computing paradigms.

Revolutionary Advanced Features

• AI-Driven Clustering Selection - Automatically select optimal clustering algorithms
• Quantum-Neuromorphic Clustering - Fusion of quantum and spiking neural networks
• Meta-Learning Optimization - Learn optimal hyperparameters from experience
• Adaptive Resource Allocation - Dynamic GPU/CPU/QPU resource management
• Multi-Objective Clustering - Optimize for accuracy, speed, and interpretability
• Continual Learning - Adapt to changing data distributions in real-time
• Bio-Quantum Clustering - Nature-inspired quantum clustering algorithms

Advanced AI Techniques

• Transformer-Based Cluster Embeddings - Deep representations of cluster patterns
• Graph Neural Networks - Understand complex data relationships
• Reinforcement Learning - Learn optimal clustering strategies
• Neural Architecture Search - Automatically design optimal clustering networks
• Quantum-Enhanced Optimization - Leverage quantum superposition and entanglement
• Spike-Timing Dependent Plasticity - Bio-inspired adaptive clustering
• Memristive Computing - In-memory quantum-neural computations

Examples

use scirs2_cluster::advanced_clustering::{AdvancedClusterer, QuantumNeuromorphicMetrics};
use scirs2_core::ndarray::array;
use scirs2_cluster::error::Result;

// AI-driven Advanced clustering
let data = array![[0.0, 0.0], [1.0, 0.0], [0.0, 1.0], [1.0, 1.0], [5.0, 5.0], [6.0, 5.0]];
let mut advanced = AdvancedClusterer::new()
    .with_ai_algorithm_selection(true)
    .with_quantum_neuromorphic_fusion(true)
    .with_meta_learning(true)
    .with_continual_adaptation(true)
    .with_multi_objective_optimization(true);

let result = advanced.cluster(&data.view())?;
println!("Advanced clusters: {:?}", result.clusters);
println!("AI advantage: {:.2}x speedup", result.ai_speedup);
println!("Quantum advantage: {:.2}x optimization", result.quantum_advantage);

Structs

AIClusteringSelector

AdvancedClusterer

Advanced clusterer with AI-driven quantum-neuromorphic algorithms

AdvancedClusteringResult

Advanced clustering result

AdvancedConfig

Configuration for Advanced clustering

AdvancedPerformanceMetrics

Performance metrics for Advanced clustering

AlgorithmSelectionNetwork

BioplasticityRules

ClusteringKnowledgeBase

ClusteringPerformanceRecord

ClusteringRLAgent

ContinualAdaptationEngine

DataCharacteristics

FewShotClusterLearner

MAMLParameters

MetaLearningClusterOptimizer

MetaLearningEpisode

NeuromorphicParameters

OptimizationParameters

PerformancePredictionModel

QuantumClusterState

Global quantum state for cluster superposition

QuantumNeuromorphicMetrics

QuantumNeuromorphicProcessor

QuantumSpikingNeuron

Quantum-enhanced spiking neuron for clustering

TransferLearningEngine