# Formal Context Generation using Dirichlet Distributions

arXiv: https://arxiv.org/abs/1809.11160 · HTML: https://mlsanity.com/paper/1809.11160/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Sep 2018.

## Built on by (most cited first, 3 of 3)

- [Towards Ordinal Data Science](https://mlsanity.com/paper/2307.09477/) (2023)
- [Identifying Influential Nodes in Two-mode Data Networks using Formal Concept Analysis](https://mlsanity.com/paper/2109.03372/) (2021)
- [Detecting Important Patterns Using Conceptual Relevance Interestingness Measure](https://mlsanity.com/paper/2110.11262/) (2021)

## Abstract (arXiv)

We suggest an improved way to randomly generate formal contexts based on Dirichlet distributions. For this purpose we investigate the predominant way to generate formal contexts, a coin-tossing model, recapitulate some of its shortcomings and examine its stochastic model. Building up on this we propose our Dirichlet model and develop an algorithm employing this idea. By comparing our generation model to a coin-tossing model we show that our approach is a significant improvement with respect to the variety of contexts generated. Finally, we outline a possible application in null model generation for formal contexts.