SAM Anomaly Detection Algorithms: Complete Catalog
Overview
SAM provides access to 7+ state-of-the-art anomaly detection algorithms, ranging from traditional statistical methods to cutting-edge neural networks. Our SAM (Systematic Agentic Modeling) system automatically selects the optimal combination based on your data characteristics, ensuring maximum accuracy and reliability.
Algorithm Categories
Distance-Based Methods - Isolation & Proximity
Algorithms that identify anomalies based on distance from normal data patterns.
Boundary-Based Methods - Decision Boundaries
Advanced techniques that create optimal separation boundaries between normal and anomalous data.
Density-Based Methods - Local Density Analysis
Methods that detect anomalies in regions of low data density or unusual local patterns.
Reconstruction-Based Methods - Pattern Learning
Neural networks and dimensionality reduction techniques that identify anomalies through reconstruction error.
Distance-Based Methods
Isolation Forest
Best For: Large datasets with mixed data types, enterprise-scale detection
- Strengths: Excellent scalability, handles mixed data types, minimal assumptions
- Data Requirements: Minimum 100 observations, works with categorical and numerical data
- Processing Time: Fast (1-3 minutes for most datasets)
- Use Cases: Fraud detection, system monitoring, quality control
How It Works:
- Creates random binary trees that isolate data points
- Anomalies are isolated with fewer tree splits than normal points
- Highly efficient for large datasets with linear time complexity
When to Use:
- Large datasets (1000+ records)
- Mixed data types (numerical + categorical)
- Need fast, scalable detection
- High-dimensional data scenarios
Local Outlier Factor (LOF)
Best For: Local anomaly detection, neighborhood-based analysis
- Strengths: Excellent local anomaly detection, intuitive scoring, flexible density estimation
- Data Requirements: Minimum 50 observations, works best with continuous data
- Processing Time: Medium (2-5 minutes depending on data size)
- Use Cases: Customer behavior analysis, network intrusion detection, sensor monitoring
How It Works:
- Compares local density of each point to its neighbors
- Identifies points with significantly lower density than their neighborhoods
- Provides interpretable anomaly scores based on local context
When to Use:
- Need to detect local anomalies (not just global outliers)
- Data has varying density regions
- Interpretable anomaly scores required
- Medium-sized datasets (100-10,000 records)
Boundary-Based Methods
One-Class SVM
Best For: Complex decision boundaries, high-dimensional data
- Strengths: Robust boundary detection, kernel flexibility, theoretical foundation
- Data Requirements: Minimum 200 observations, benefits from feature scaling
- Processing Time: Medium-High (3-10 minutes with kernel optimization)
- Use Cases: Text analysis, image processing, high-dimensional anomaly detection
How It Works:
- Creates optimal hyperplane separating normal data from anomalies
- Uses kernel functions for non-linear boundary detection
- Maximizes margin around normal data region
When to Use:
- High-dimensional data (>20 features)
- Complex non-linear patterns
- Need robust decision boundaries
- Sufficient training data available
Kernel Options:
- RBF (Radial Basis Function): Best for non-linear patterns
- Linear: Fast processing for linear separability
- Polynomial: Good for structured data with polynomial relationships
Support Vector Data Description (SVDD)
Best For: Spherical boundary detection, robust outlier handling
- Strengths: Minimal volume enclosing sphere, robust to parameter settings
- Data Requirements: Minimum 100 observations, works with normalized data
- Processing Time: Medium (2-6 minutes)
- Use Cases: Quality control, process monitoring, equipment diagnostics
How It Works:
- Creates minimal spherical boundary around normal data
- Optimizes sphere radius to minimize volume while containing target data
- Identifies anomalies outside the spherical boundary
When to Use:
- Data clusters in spherical patterns
- Need simple geometric interpretation
- Robust detection with minimal parameter tuning
- Process control applications
Density-Based Methods
HDBSCAN (Hierarchical DBSCAN)
Best For: Clustering-based anomaly detection, variable density patterns
- Strengths: Handles varying densities, identifies noise points, hierarchical structure
- Data Requirements: Minimum 100 observations, works with distance-based features
- Processing Time: Medium (3-8 minutes for complex datasets)
- Use Cases: Customer segmentation, geographic analysis, behavioral clustering
How It Works:
- Creates hierarchical clustering based on point density
- Identifies points that don't belong to any dense cluster as anomalies
- Adapts to varying density levels automatically
When to Use:
- Data has natural clustering structure
- Variable density patterns exist
- Need to identify both anomalies and clusters
- Geographic or spatial data analysis
Key Parameters:
- MinPts: Minimum points required for cluster formation
- Cluster Selection: Stability-based optimal cluster selection
- Distance Metric: Euclidean, Manhattan, or custom distance functions
Reconstruction-Based Methods
Autoencoder Neural Network
Best For: Complex pattern learning, high-dimensional data, non-linear relationships
- Strengths: Learns complex patterns, handles non-linear relationships, interpretable reconstruction errors
- Data Requirements: Minimum 500 observations, benefits from GPU acceleration
- Processing Time: High (5-15 minutes with neural network training)
- Use Cases: Image analysis, sensor data, complex behavioral patterns
How It Works:
- Neural network learns to reconstruct normal data patterns
- Anomalies produce higher reconstruction errors than normal data
- Multiple hidden layers capture complex non-linear relationships
Architecture Options:
- Shallow Autoencoder: 1-2 hidden layers for simple patterns
- Deep Autoencoder: 3+ layers for complex pattern learning
- Variational Autoencoder: Probabilistic approach with uncertainty quantification
When to Use:
- Large datasets with complex patterns
- High-dimensional data (>50 features)
- Non-linear relationships in data
- GPU resources available for training
PCA-Based Detection
Best For: Dimensionality reduction, linear pattern analysis
- Strengths: Fast processing, interpretable components, handles correlated features
- Data Requirements: Minimum 100 observations, works with numerical data
- Processing Time: Fast (30 seconds - 2 minutes)
- Use Cases: Financial analysis, process monitoring, data quality assessment
How It Works:
- Reduces data to principal components capturing most variance
- Calculates reconstruction error from reduced representation
- High reconstruction errors indicate anomalous patterns
When to Use:
- High correlation among features
- Need fast, interpretable results
- Linear relationships dominate
- Baseline anomaly detection required
Ensemble Methods
Multi-Algorithm Consensus
Best For: Maximum reliability, reduced false positives, comprehensive detection
- Strengths: Combines multiple algorithm strengths, reduces bias, improves robustness
- Processing Time: Variable (sum of selected algorithms)
- Use Cases: Critical applications, fraud detection, security monitoring
Consensus Strategies:
- Voting: Simple majority or weighted voting across algorithms
- Score Averaging: Mean or median of normalized anomaly scores
- Rank Aggregation: Consensus ranking of most anomalous points
Adaptive Ensemble
Best For: Dynamic algorithm selection, changing data patterns
- Strengths: Adapts to data characteristics, optimizes performance automatically
- Processing Time: Variable based on selected algorithms
- Use Cases: Evolving datasets, multi-domain analysis, production environments
Algorithm Selection Guide
Automatic Selection Criteria
Our SAM system selects algorithms based on these data characteristics:
For Large Datasets (1000+ records)
- Isolation Forest - Excellent scalability and mixed data handling
- One-Class SVM - Robust boundary detection with kernel flexibility
- HDBSCAN - Efficient clustering-based detection
- Autoencoder - Complex pattern learning with neural networks
For High-Dimensional Data (20+ features)
- PCA-Based Detection - Dimensionality reduction benefits
- Autoencoder - Non-linear dimensionality handling
- One-Class SVM - Kernel methods for high dimensions
- Isolation Forest - Random feature selection advantages
For Mixed Data Types
- Isolation Forest - Native mixed-type handling
- HDBSCAN - Distance-based approach with custom metrics
- Local Outlier Factor - Flexible distance computations
- Ensemble Methods - Multiple algorithm perspectives
For Real-Time Applications
- Isolation Forest - Fast linear-time detection
- PCA-Based - Minimal computational overhead
- Pre-trained Models - Cached algorithm parameters
- Simple Thresholding - Statistical outlier detection
For Maximum Accuracy
- Ensemble Voting - Multi-algorithm consensus
- Autoencoder - Complex pattern learning
- One-Class SVM - Optimized boundary detection
- Adaptive Selection - Data-specific optimization
Performance Matrix
| Algorithm | Accuracy | Speed | Scalability | Interpretability | Data Types |
|---|---|---|---|---|---|
| Isolation Forest | High | Very High | Excellent | Medium | Mixed |
| One-Class SVM | High | Medium | Good | Low | Numerical |
| LOF | High | Medium | Fair | High | Numerical |
| HDBSCAN | Medium | Medium | Good | High | Distance-based |
| Autoencoder | Very High | Low | Good | Medium | Numerical |
| PCA-Based | Medium | Very High | Excellent | High | Numerical |
| Ensemble | Very High | Variable | Good | Medium | All Types |
GPU Acceleration
Supported Algorithms
Neural network and computationally intensive algorithms benefit from GPU acceleration:
- Autoencoder: 5-10x faster training and inference
- One-Class SVM: 3-5x faster with kernel computations
- PCA-Based: 2-3x faster with matrix operations
- Ensemble Methods: Parallel algorithm execution
Performance Benefits
- Reduced Processing Time: Minutes instead of hours for complex datasets
- Larger Model Capacity: Handle more complex patterns and larger datasets
- Batch Processing: Multiple detection tasks simultaneously
- Real-time Updates: Faster model retraining and adaptation
How SAM Selects Algorithms
Intelligent Algorithm Selection Process
SAM automatically chooses optimal anomaly detection algorithms through a 3-step AI-driven process:
Step 1: Data Characterization
Our system analyzes your dataset across multiple dimensions:
- Size and Dimensionality: Records count and feature space analysis
- Data Types: Numerical, categorical, mixed type assessment
- Distribution Properties: Statistical patterns and assumptions validation
- Quality Metrics: Completeness, noise levels, and consistency evaluation
Step 2: Algorithm Scoring
Each available algorithm receives a suitability score (0-10):
- Distance-Based Methods: Optimal for large, mixed datasets
- Boundary-Based Methods: Best for high-dimensional, complex patterns
- Density-Based Methods: Ideal for clustering and local anomaly detection
- Reconstruction-Based: Perfect for complex non-linear relationships
Step 3: Smart Selection
The AI optimizes for both accuracy and efficiency:
- Balanced Portfolio: Combines different algorithm types for robustness
- Optimal Count: Selects 1-4 algorithms based on data complexity and requirements
- Performance Priority: Balances accuracy with processing speed
- Resource Optimization: Considers available computational resources
Selection Examples
Large E-commerce Dataset (50K records, 25 features)
- Selected: Isolation Forest + One-Class SVM + Ensemble
- Reason: Scalability needs with robust boundary detection
- Expected: High accuracy with 3-5 minute processing time
Small Financial Dataset (500 records, 8 features)
- Selected: LOF + PCA-Based + Statistical Methods
- Reason: Local patterns important, need interpretable results
- Expected: Good accuracy with 1-2 minute processing time