Formal Context Generation using Dirichlet Distributions

TL;DR

This paper introduces a novel method for generating formal contexts using Dirichlet distributions, addressing limitations of traditional coin-tossing models and enhancing the diversity of generated contexts for applications like null model creation.

Contribution

The paper proposes a Dirichlet-based model for formal context generation, improving diversity over existing coin-tossing methods and providing an algorithm for implementation.

Findings

Dirichlet model produces more diverse formal contexts.

Compared to coin-tossing, the new model significantly enhances variety.

Potential application in null model generation for formal contexts.

Abstract

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.