fp_growth 1.0.2
fp_growth: ^1.0.2 copied to clipboard
A high-performance Dart FP-Growth algorithm for frequent pattern mining and association rule learning, with CLI and CSV support.
1.0.2 #
- test(property): Add property-based tests for input order independence and support monotonicity
- fix(core): Fix determinism issue by enforcing stable sorting for equal-frequency items
- test(stress): Add stress and scale tests (10k, 100k, single large transaction)
- docs(benchmarks): Update performance figures using compiled executables instead of dart run
- docs(readme): Update README with CLI usage and performance benchmarks
- chore(changelog): Add 1.0.2 release notes
1.0.1 #
- docs(readme): Update README with CLI usage and performance benchmarks
- refactor(fp-growth): Extract parallel mining to platform modules
Move isolate-based parallel mining into a dedicated
parallel_runner.dartfile. Provide a web-specificparallel_runner_web.dartthat falls back to single-threaded execution. Adjustfp_growth.dartto use conditional imports for platform-aware parallelism and promote internal helper functions for reuse. - chore(changelog): Add 1.0.1 release notes
1.0.0 #
- Initial release of the fp_growth package.
- Implemented the full FP-Growth algorithm for frequent itemset mining.
- Added association rule generation with Support, Confidence, and Lift metrics.
- Introduced internal integer-item mapping for improved performance.
- Implemented single-path optimization in recursive mining.
- Added a CSV data adapter for loading transaction datasets.
- Developed a Command-Line Interface (CLI) for effortless dataset analysis.
- Integrated a flexible and extensible logging system.
- Refactored and reorganized the project structure for better maintainability.
- Enhanced association rules with additional metrics: Leverage and Conviction.