Mondher

Find the hidden structure in your data.

A working tool for Formal Concept Analysis — upload a binary context, compute its concept lattice, and explore the implications between attributes. Built for researchers who need results they can cite.

View source on GitHub →

Four ways to use it

One engine, four surfaces — pick the one that fits your workflow.

The same Rust core powers every surface. Switch between them as your needs change — start in the web app, integrate via the API, embed via WASM, automate via Python.

Web app

For interactive exploration

You're using it. Upload a context, compute its lattice, browse implications, share the result. No install needed.

Python

For Jupyter notebooks and scripts

A PyO3-built wheel with Jupyter SVG rendering. Compute lattices in notebooks, integrate with pandas, automate research pipelines.

$ pip install mondher

JavaScript

For web embeds and Node tools

WebAssembly bindings (~250 KB gzipped) that run client-side. Compute and render lattices in any browser or Node 20+ app.

$ npm install mondher

HTTP API

For any language

A REST API documented with OpenAPI. POST a context, compute its lattice or implications, get results as JSON. Works from any HTTP client.

$ cargo run -p mondher-api

What it does

A complete FCA workflow, from upload to citation.

Lattices

Three algorithms. NextClosure, In-Close 5, FCbO. Identical output. Choose the one that fits your data shape.

Implications

The Duquenne-Guigues canonical base, computed and filterable. The smallest set of rules that captures every dependency.

Citable

BibTeX export, persistent share links, SVG and PNG of every lattice. Your results survive the paper.