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Clustering System Architecture

Overview​

SAM's clustering system combines AI-driven model selection, parallel processing, and comprehensive business intelligence to deliver scalable, accurate clustering across diverse datasets and business applications.

System Architecture​

High-Level Architecture Diagram​

High-Level Architecture Diagram

Core Components​

1. Data Processing & Cleaning Layer​

  • Data Quality Assessment: Missing value analysis, outlier detection, duplicate identification
  • Format Standardization: Date formats, currency symbols, text encoding consistency
  • Data Type Conversion: Proper numeric conversion, categorical encoding
  • Business Rule Validation: Revenue validation, date checks, logical consistency

2. Feature Aggregation Layer​

  • Multi-Level Aggregation: Store, Product, and Geographic level data aggregation
  • Feature Engineering: Time-series features, spatial analysis, business metrics calculation
  • Data Transformation: Revenue aggregation, margin analysis, performance ratios
  • Post-Aggregation Processing: Velocity calculations, growth rates, efficiency metrics

3. Advanced Data Pre-Processing Layer​

  • File Parsing: CSV and Excel file processing with automatic data type recognition
  • Data Validation: Dataset format validation and business rule verification
  • Feature Engineering: Automated feature selection, scaling, and transformation
  • Data Preparation: Missing value handling, outlier detection, and dimensionality reduction

4. AI Intelligence Engine​

  • Model Selection: AI-driven evaluation and selection of optimal clustering algorithms
  • Data Characterization: Statistical analysis of dataset properties and clusterability
  • Performance Prediction: Expected accuracy and processing time estimation for each model
  • Ensemble Optimization: Intelligent combination of complementary clustering approaches

5. Processing Engine​

  • Background Execution: Non-blocking processing with real-time status tracking
  • Multi-Model Processing: Parallel execution of selected clustering algorithms
  • Hyperparameter Optimization: Automated parameter tuning using advanced optimization
  • Resource Management: Dynamic CPU/GPU allocation and memory optimization

6. Business Intelligence Layer​

  • Result Processing: Multi-model ensemble scoring with confidence assessment
  • Visual Analytics: Chart generation showing cluster separation and characteristics
  • Report Generation: Executive PDF reports with findings and business recommendations
  • Business Metrics: Cluster quality analysis, profit contribution calculation, and strategic insights

7. LLM Analysis Pipeline​

  • Data Integration: Merges clustering results with complete business datasets
  • AI Processing: Multi-stage LLM analysis for cluster naming and profiling
  • Business Intelligence: Strategic role assignment and executive summaries
  • Visualization Pipeline: Advanced chart generation and report compilation

8. Model Integrity & Quality Assurance​

  • Cross-Validation Engine: Rigorous cluster quality testing and performance validation
  • Consensus Scoring: Multi-algorithm agreement assessment for reliability determination
  • Quality Gates: Automated checks ensuring only validated models reach production
  • Business Logic Validation: Results verification against domain knowledge and constraints
  • Confidence Assessment: Real-time reliability scoring and uncertainty quantification

SAM Clustering Processing​

SAM Clustering Processing Architecture

Data Flow Architecture​

Processing Pipeline​

Data Processing Pipeline

Background Processing System​

Asynchronous Execution:

  • Non-Blocking Operations: User interface remains responsive during clustering processing
  • Status Monitoring: Real-time progress updates and processing transparency for users
  • Queue Management: Efficient handling of multiple concurrent clustering requests
  • Error Recovery: Graceful handling of processing failures with automatic retry mechanisms