postmining
Postmining is a phase in data mining and knowledge discovery processes that follows the initial discovery of patterns, associations, or models. It focuses on validating, interpreting, and refining results to ensure reliability and actionable value. Postmining aims to separate meaningful findings from incidental artifacts introduced by data noise, sampling, or modeling choices. The concept is used across domains such as market analysis, bioinformatics, text mining, and social science.
Activities typically involved in postmining include:
- Validation on independent data or holdout sets, including cross-validation
- Statistical significance testing and assessment of robustness against noise and overfitting
- Redundancy reduction and consolidation of similar patterns or rules
- Ranking and selection based on domain-relevant criteria and interestingness measures
- Domain expert evaluation and interpretation to ensure practical relevance
- Post-processing transformations such as rule pruning, aggregation, or summarization
- Visualization and reporting, metadata capture, and provenance for reproducibility
- Monitoring and re-evaluation as new data arrives, addressing concept drift or model updates
Outputs and governance in postmining emphasize documentation, reproducibility, and traceability. This includes recording evaluation results, providing
Relation to other terms: Postmining is sometimes referred to as post-processing or post-discovery validation. It is