patternshigh
Patternshigh is a framework and family of methodologies for discovering and interpreting high-level patterns in large and diverse data sets. It integrates pattern mining, representation learning, and explainability to enable analysts to identify recurring motifs, structures, or behaviors across domains. The emphasis is on compositional abstractions that remain interpretable to humans rather than opaque low-level features.
Origin and scope: The term Patternshigh appeared in the early 2020s within academic and industry discussions
Architecture: A typical Patternshigh approach includes data ingestion, a pattern engine, an intermediate representation for patterns,
Algorithms and representations: Methods cover time-series motif discovery, graph pattern mining, rule-based abstractions, and learned representations
Applications: Potential use cases include finance for detecting recurring market patterns, cybersecurity for intrusion motifs, healthcare
Limitations and governance: Patternshigh faces computational complexity and risks of overfitting or misinterpretation if domain knowledge