mdl-fca

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Learning "good" concept DAGs from binary data by minimum description length — a probabilistic/information-theoretic reworking of Formal Concept Analysis.

Plain FCA maps a binary object×attribute context to a concept lattice, but it is not a probabilistic model: it creates a concept for every random coincidence, so the lattice explodes. This project builds instead the small DAG of concepts that best compresses the data: a concept exists only if it pays for its own description. Concepts can have multiple parents (unlike hierarchical clustering's trees) and are organized hierarchically (unlike flat topic models).

Status: design phase complete, prototype under construction.

  • Read CLAUDE.md for orientation and design commitments.
  • Read docs/ in numeric order for the full design:
    1. background and goal
    2. model and codelength (the core)
    3. algorithm (greedy pair-merge constructor + search framework)
    4. batch vs online
    5. prototype spec (current build target)
    6. roadmap

Quick start (once the prototype exists)

pip install -e ".[test]"
pytest                       # 15 tests
python examples/run_planted.py

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Data verified by PyPI on Sep 11, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License

BSD-3-Clause

Requires

Python >=3.11

Provides Extra

test demos dev

Tags

fca formal-concept-analysis formal concept analysis minimum-description-length mdl concept-lattice unsupervised unsupervised-learning unsupervised learning graph graph-learning graph learning dag dag-learning dag learning lattice-learning lattice learning information-theory knowledge-graph

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