Teradata Silhouette Analysis

Core Capabilities

• Automated table structure analysis via DBC.ColumnsV
• Dynamic SQL generation for TD_Silhouette
• Complete workflow from data preparation to results interpretation
• Data quality validation and preprocessing guidance
• Parameter optimization and tuning

Key Parameters

• TargetColumns: Feature columns used for clustering
• ClusterIDColumn: Column containing cluster assignments
• IDColumn: Unique row identifier

Use Cases

1. Clustering quality assessment
2. Optimal K selection for K-Means
3. Cluster separation measurement
4. Comparing clustering algorithms

Example Usage

-- TD_Silhouette execution
SELECT * FROM TD_Silhouette (
    ON {USER_DATABASE}.{CLUSTERED_TABLE} AS InputTable
    USING
    TargetColumns ('{FEATURE_COLUMNS}')
    ClusterIDColumn ('{CLUSTER_ID_COLUMN}')
    IDColumn ('{ID_COLUMN}')
) AS dt;

Scripts Included

Core Analytics Scripts

• table_analysis.sql: Automatic table structure discovery
• preprocessing.sql: Data preparation and feature engineering
• model_training.sql: TD_Silhouette execution
• evaluation.sql: Results analysis and metrics
• complete_workflow_template.sql: End-to-end workflow

Utility Scripts

• data_quality_checks.sql: Comprehensive data validation
• parameter_tuning.sql: Parameter optimization
• diagnostic_queries.sql: Results diagnostics and interpretation

Best Practices

• Always run table_analysis.sql first to understand your data structure
• Validate data quality before executing the analytical function
• Use parameter_tuning.sql to find optimal configuration
• Review diagnostic_queries.sql output for model/results validation

Limitations

• Requires Teradata Vantage 17.20+ with ClearScape Analytics
• Input data must meet function-specific requirements
• Results depend on data quality and parameter configuration

Teradata Silhouette Analysis - ClearScape Analytics skill for Teradata Vantage